This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.

# removes all variables but NOT functions
rm(list = setdiff(ls(), lsf.str()))
knitr::opts_chunk$set(echo = FALSE)
library(magrittr)
library(ggplot2)
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union
library(ggplot2)
library(lme4)
Loading required package: Matrix
library(tidyr)

Attaching package: ‘tidyr’

The following object is masked from ‘package:Matrix’:

    expand

The following object is masked from ‘package:magrittr’:

    extract
library(merTools) 
Loading required package: arm
Loading required package: MASS

Attaching package: ‘MASS’

The following object is masked from ‘package:dplyr’:

    select


arm (Version 1.9-3, built: 2016-11-21)

Working directory is /Users/djw/Dropbox/PROGRAMMING/_NEURO/2017_MADE/Analysis
library(lsr)
library(reshape2)

Attaching package: ‘reshape2’

The following object is masked from ‘package:tidyr’:

    smiths
library(ggpubr)
library(plyr)
--------------------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
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Attaching package: ‘plyr’

The following objects are masked from ‘package:dplyr’:

    arrange, count, desc, failwith, id, mutate, rename, summarise, summarize
library(Hmisc) # cut2
Loading required package: lattice
Loading required package: survival
Loading required package: Formula

Attaching package: ‘Hmisc’

The following objects are masked from ‘package:plyr’:

    is.discrete, summarize

The following objects are masked from ‘package:dplyr’:

    combine, src, summarize

The following objects are masked from ‘package:base’:

    format.pval, round.POSIXt, trunc.POSIXt, units
library(RColorBrewer)
specify_decimal <- function(x, k) trimws(format(round(x, k), nsmall=k))

Image Swap Version

Analysis of the MULTIPLIER trials first

still need to work out how to select subject if the NM trials based on their performance in the multiplier trials

Dataframe header

# set WD
setwd("~/Dropbox/PROGRAMMING/_NEURO/2017_MADE/Analysis")
load("Data/S_M.Rdata")
load("Data/S_M_raw.Rdata")
load("Data/NS_M.Rdata")
load("Data/S_NM.Rdata")
load("Data/S_NM_raw.Rdata")
head(S_M_raw)
mean(S_NM$rt)
mean(S_NM$accuracy)
mean(S_NM$swapAmount)

mean(S_M$rt)
mean(S_M$accuracy)
sd(S_M$accuracy)
mean(S_M$swapAmount)


subject_means <- group_by(S_M_raw, subject) %>%
  dplyr::summarize(swaps = mean(swapAmount, na.rm =T))
plot(subject_means)

subject_means <- group_by(S_M_raw, subject) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T), finalEarnings = mean(finalEarnings, na.rm = T))

Table of subject count, mean, median, SD, range, skew, kurtosis

Experiment 1/2

Accuracy, rt

Uncleaned/Cleaned
load("Data/made_descripive_stats.Rdata")

Add all DFs (cleaned), mark by condition

Plot boxplots of rt and accuracy

load("Data/NS_NM.Rdata")
load("Data/NS_M.Rdata")
load("Data/S_NM.Rdata")
load("Data/S_M.Rdata")
NS_NM$type = "NoSwap_NoMult"
NS_M$type = "NoSwap_Mult"
S_NM$type = "Swap_NoMult"
S_M$type = "Swap_Mult"
# Join all DFs
common <- intersect(names(NS_NM), names(NS_M))
df = rbind(NS_NM[,common], NS_M[,common])
common <- intersect(names(S_NM), names(S_M))
df2 = rbind(S_NM[,common], S_M[,common])
common <- intersect(names(df), names(df2))
df_full = rbind(df[,common], df2[,common])
# Boxplot RT
boxplot(rt ~ factor(type),
        varwidth = TRUE, xlab = "Trial Type",
        main = "RT by Trial Type", ylab = "RT in s", data = df_full)

# Barplot of Accuracy
#First get means for each trial condition (type) by Subject
d <- df_full
subject_means <- group_by(d, type) %>%
  dplyr::summarize(accuracy = mean(correct, na.rm = T))
subject_means$accuracy = round(subject_means$accuracy, 3)
#PLOT
lower=c(0.787990, 0.787694, 0.798404, 0.759005) 
upper=c(0.877638, 0.867699, 0.880029, 0.893729)
barplot <- ggplot(subject_means, aes(x = type, y = accuracy)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean",
    col = "black",
    fill = "gray70"
  ) +
  #geom_point(position = position_jitter(h = 0, w = 0.2)) +
  scale_y_continuous(limits = c(0, max(d$correct, na.rm = T)),
                     expand = c(0, 0)) +
  geom_text(aes(label=accuracy), position=position_dodge(width=0.9), vjust=-0.25, hjust=-0.65) +
  geom_errorbar(data=subject_means, mapping=aes(x=type, ymin=upper, ymax=lower), width = 0.3)
barplot + ggtitle("Accuracy by Trial Type")

T-Test NS_M vs S_M accuracy

t.test(NS_M$correct, S_M$correct)

t.test(NS_NM$rt[NS_NM$Trial<51], NS_NM$rt[NS_NM$Trial>50])

subject_means <- group_by(S_M_raw, subject) %>%
  dplyr::summarize(swapAvg = mean(swapAvg), finalEarnings = mean(finalEarnings, na.rm = T))
x = subject_means$swapAvg[subject_means$swapAvg>1.6]
length(x)
length(subject_means$subject)

T-test NS vs S RT

t.test(subject_means$rt[subject_means$study == "Standard Mult."], subject_means$rt[subject_means$study=="Swap Mult."])

    Welch Two Sample t-test

data:  subject_means$rt[subject_means$study == "Standard Mult."] and subject_means$rt[subject_means$study == "Swap Mult."]
t = -2.8654, df = 35.986, p-value = 0.006914
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.1442602 -0.1957772
sample estimates:
mean of x mean of y 
 2.361594  3.031612 
There were 50 or more warnings (use warnings() to see the first 50)

Summed Val vs. Accuracy

datac <- summarySEwithin(df, measurevar="correct", withinvars=c("multDif","difficulty"), idvar="subject")
Automatically converting the following non-factors to factors: multDif, difficulty
ggplot(datac, aes(x=difficulty, y=correct, fill=multDif)) +
    geom_bar(position=position_dodge(.9), colour="black", stat="identity") +
    geom_errorbar(position=position_dodge(.9), width=.25, aes(ymin=correct-ci, ymax=correct+ci)) +
    coord_cartesian(ylim=c(0.0,1)) +
    labs(y = "Accuracy", x = "Difficulty (net value)") +
    scale_x_discrete(labels=c("1" = "Easy (>=0.5)", "2" = "Difficult (<0.5)")) +    
    scale_y_continuous(breaks=seq(0,1,0.1)) +
    theme_bw() +
    theme(axis.title.x=element_text(size=18),
        axis.title.y = element_text(size = 18)) +
    scale_fill_brewer(palette="Pastel2") +
    guides(fill=guide_legend(title="Difference\nbetween\nMultipliers"))
setwd("/Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots")
The working directory was changed to /Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the the working directory for notebook chunks.
ggsave("MultDif.pdf", width = 16, height = 12, units = "cm")

Summed val vs. RT

df <- S_M
There were 50 or more warnings (use warnings() to see the first 50)
#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=summedVal, y=rt, group = factor(multNum), colour = factor(multNum)), df) +
  #geom_smooth(aes(x=summedVal, y=rt, colour = "flip"), subset(df, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #ggtitle("RT vs. Summed Val")
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth() +  # Add a loess smoothed fit curve with confidence region
  theme_minimal()+
  guides(colour=guide_legend("Multiplier \nCondition")) +
  scale_x_continuous(name="Net Value ($)", seq(-3,3,0.5), limits = c(-3,3))+
  scale_y_continuous(name = "Reaction Time (s)")

setwd("/Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots/")
The working directory was changed to /Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the the working directory for notebook chunks.
ggsave("2ndFix.pdf", width = 18, height = 12, units = "cm")

Second Fixation vs Summed Value

df <- S_M
#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=summedVal, y=`2_fixation`, group = factor(secondMult), colour = factor(secondMult)), df) +
  #geom_smooth(aes(x=summedVal, y=logRT, colour = "flip"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #ggtitle("Second Fixation vs Summed Value")
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  +# Add a loess smoothed fit curve with confidence region
  theme_minimal()+
  guides(colour=guide_legend("Multiplier \nCondition")) +
  scale_x_continuous(name="Trial Net Value ($)", seq(-3,3,0.5), limits = c(-3,3))+
  scale_y_continuous(name = "Fixation Duration (s)")

#Test for SIG
summary(lm(`2_fixation`~summedVal + factor(secondMult), df))

Call:
lm(formula = `2_fixation` ~ summedVal + factor(secondMult), data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.0029 -0.3556 -0.1513  0.1609  6.7574 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)          1.034731   0.009812 105.452  < 2e-16 ***
summedVal           -0.012915   0.006777  -1.906   0.0567 .  
factor(secondMult)2  0.115010   0.019281   5.965 2.57e-09 ***
factor(secondMult)3  0.184020   0.019372   9.499  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.6114 on 6576 degrees of freedom
  (17 observations deleted due to missingness)
Multiple R-squared:  0.01601,   Adjusted R-squared:  0.01556 
F-statistic: 35.67 on 3 and 6576 DF,  p-value: < 2.2e-16

QUESTIONNAIRES

Boxplot of unfiltered data

You can see which people were not trying:

df <- S_M_raw

# Filter out extremely long rt times
df <- df[!(df$rt>10),]

boxplot(rt ~ factor(subject),
        varwidth = TRUE, xlab = "subject",
        main = "Boxplot of RT conditional on\
        subject", ylab = "RT", data = df)

Plotting mean RT vs. Earnings (unfiltered data):

KMeans to Divide into groups based on swaps/$$ (unfiltered data):

Tends to include some people who earned over $50 but never looked at the second image…

Clean data by removing people with less than 1.5 swap average and less than $0 earnings

#remove earnings below 0
total_M_clean2 <- S_M_raw[!(S_M_raw$finalEarnings<0),]
#remove flip avgs less than 1.5
total_M_clean2 <- total_M_clean2[!(total_M_clean2$flipAvg<1.5),]
length(unique(total_M_clean2$subject))

#MORE AGRESSIVE: REMOVE BELOW 75%
total_M_clean3 <- S_M_raw[!(S_M_raw$accuracy<0.75),]
length(unique(total_M_clean3$subject))

Taking out those that swapped images less than 2 times and earned less than $0 leaves 24 subjects Taking out those that had an accuracy of below 75% leaves 21 subjects

Boxplot of filtered data

A lot of variation in avg. RT and the variance of RTs. 4,7 and 23 seem to have particularly low variance.

boxplot(rt ~ factor(subject),
        varwidth = TRUE, xlab = "subject",
        main = "Boxplot of RT conditional on\
        subject", ylab = "RT", data = S_M)

Check RT distribution

Skewed right, as is typical of RT.

Histogram of

There were 41 warnings (use warnings() to see them)
is.na() applied to non-(list or vector) of type 'NULL'is.na() applied to non-(list or vector) of type 'NULL'

RTs By Subject


g = ggplot(S_M, aes(x = rt)) + geom_histogram() 

g + facet_wrap(~ subject, ncol=2)

Hist of Middle Fixations (All Subjects)

df <- S_M

#create version of dataset with only middle fixations (we use this later for drawing random middle fixations)

# make sure every trial ends with an NA val
df$`14_fixation` <- NA     

# find col Number of first fix
# Check that we are starting at column fix#1
firstFix = which(names(df) == "1_fixation")

# find the first NA val in each row
naVal <- vector(mode="numeric", length=0)
for(x in 1:length(df$Trial)){
  naVal[x] <- min(which(is.na(df[x,])))
}

# if naVal >39 then there were at least 3 fixations
midFix <- vector(mode = "numeric", length = 0)
for(x in 1:length(df$Trial)){
  if(naVal[x]>(firstFix+2)){
    for(y in (firstFix+1):(naVal[x]-2)){
      val = df[x,y]
      midFix <- c(midFix, val)
    }
  }
}

mean(midFix)
median(midFix)

S_M$finalFix
hist(midFix,
   col=rgb(1,0,0,0.5), breaks=seq(0,9,0.1), ylim=c(0,900), xlab="RT", main = "Middle Fixations")
#abline(v=median(midFix), col="blue")

legend("topright",
       c(as.expression(bquote(MidFix_Median == .(median(midFix))))),
       col = c("blue"),
       lwd = c(2, 2, 1))

Hist of Middle Fixations by Subject

load("Data/S_M_K.Rdata")
df <- S_M_K

# REMOVE First and Last Fixations
df <- df[(df$fixNum!=1 & df$revFixNum!=1),]

# SUBJECT MEANS AND SD AND FIX #
subject_means <- group_by(df, subject) %>%
  dplyr::summarize(median = median(rt, na.rm = T), mean = mean(rt, na.rm = T), sd = sd(rt, na.rm = T), count = length(rt))
subject_means

# Plot Hist
g = ggplot(df, aes(x = rt)) + 
  geom_histogram(data = transform(df, subject = NULL), fill = "blue", alpha = 0.4) +
  geom_histogram() 

g + facet_wrap(~ subject, ncol=2)

# PLot Density
# "adjust" controls the bandwidth
allFix = transform(df, subject = NULL)

g = ggplot(df, aes(x=rt)) + 
  geom_density(data = allFix, aes(alpha = 0.5, fill = "group")) +
  geom_density(aes(alpha = 0.5, fill = "subject")) +
  #geom_vline(aes(xintercept=mean(rt)), color="black", size=1) +
  scale_fill_manual(name = "Density Plot", 
                    values = c(group = "blue", subject = "red"))

appender <- function(string, prefix = "Subject: ", suffix = "  Mean: ", mean = specify_decimal(subject_means$mean[subject_means$subject == (as.numeric(string))],2)) paste0(prefix, string, suffix, mean)
              
g + facet_wrap(~ subject, ncol=2, labeller = as_labeller(appender)) +
  theme(strip.text = element_text(size = 22),
        axis.text.x = element_text(size = 18),
        axis.title.x = element_text(size = 25),
        axis.text.y = element_text(size = 18),
        axis.title.y = element_text(size = 25),
        legend.text = element_text(size = 18),
        legend.title = element_text(size = 21)) +
  ggtitle("Subject vs Group Middle Fixations (in s)")

Log RT histogram

Plot RT vs Summed Value

Plot LOG RT vs Summed Value

Plot Accuracy vs Difficulty

Hist of Accuracy vs Difficulty

SIGMOID of ACCEPT VS VALUE

d <- S_M

plot(d$summedVal, d$choice,
     main = "Choice vs. Summed Val",
     xlab="Summed Val", ylab="P (Accept)",
     xlim=c(-3, 3))

model <- glm(choice ~ summedVal, data=d, family=binomial(link = logit))
summary(model)

xv <- seq(min(d$summedVal), max(d$summedVal), 0.01)
yv <- predict(model,list(summedVal=xv), type="response")

abline(0.5,0, lty=2)
lines(xv,yv)

#Find the inflection point where there is a 50/50 probability of subject accepting.
p <- 0.5
x <- (log(p/(1-p)) - coef(model)[1]) / coef(model)[2]
x

Final Fix vs Choice

TWO DIFFICULTY CONDITIONS (Hard/Easy)

Plot Hard vs. Easy for Subjects

Plot LOG RT vs Absolute Summed Value

Plot Image Swaps vs Summed Value

Plot Image Swaps vs Absolute Value

Mean Number of Fixations

Summed Value vs. Accept/Reject Decision

Logistic regression curve

RT Correct vs Incorrect Responses

#GOOD BASE FOR PLOTTING EXAMPLE

#BAR PLOT
d <- S_M
d$correct[d$correct==1] = "Correct"
d$correct[d$correct==0] = "Incorrect"

subject_means <- group_by(d, subject, correct) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T))
subject_means

#PLOT
barplot <- ggplot(subject_means, aes(x = correct, y = rt, fill=correct)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean",
    col = "black",
    stat = "identity"
  ) +
  geom_point(position = position_jitter(h = 0, w = 0.2)) +
  scale_y_continuous(limits = c(0, max(d$rt, na.rm = T)),
                     expand = c(0, 0))+
  labs(y = "RT (seconds)", x = "Response")+
  theme_minimal()+
  theme(legend.position="none") +
  ggtitle("RT vs Correct Choice")

barplot 
t.test(d$rt[d$correct=="Correct"], d$rt[d$correct=="Incorrect"])

BarPlot for Choice vs RT

#BAR PLOT
d<- S_M
d$choice[d$choice==1] = "Accept"
d$choice[d$choice==0] = "Reject"

subject_means <- group_by(d, subject, choice) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T))
subject_means

#PLOT
barplot <- ggplot(subject_means, aes(x = choice, y = rt, fill=choice)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean",
    col = "black",
    stat = "identity"
  ) +
  geom_point(position = position_jitter(h = 0, w = 0.2)) +
  scale_y_continuous(limits = c(0, max(d$rt, na.rm = T)),
                     expand = c(0, 0))+
  labs(y = "RT (seconds)", x = "Response") +
  theme_minimal() +
  theme(legend.position="none") +
  ggtitle("RT vs Choice")
  #stat_compare_means(label.y = 8.0) +
  #stat_compare_means(ref.group = "Accept", label = "p.signif", label.y = c(7.0))

barplot 

t.test(d$rt[d$choice=="Accept"], d$rt[d$choice=="Reject"])

Deciding Factor: House/Face

#BAR PLOT
d<- S_M
# Reduce DF to Decider Trials
x<- (d$faceTotal>0 & d$houseTotal<0) | (d$faceTotal<0 & d$houseTotal>0)
d = d[x==TRUE,]
# Create Decider Column
d$decider = 0
d$decider[abs(d$faceTotal)>abs(d$houseTotal)] = "Face"
d$decider[abs(d$houseTotal)>abs(d$faceTotal)] = "House"
#remove values of zero
d = d[d$decider!=0,]

subject_means <- group_by(d, subject, decider) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T))
subject_means

#PLOT
barplot <- ggplot(subject_means, aes(x = decider, y = rt, fill=decider)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean",
    col = "black",
    stat = "identity"
  ) +
  geom_point(position = position_jitter(h = 0, w = 0.2)) +
  scale_y_continuous(limits = c(0, max(d$rt, na.rm = T)),
                     expand = c(0, 0))+
  labs(y = "RT (seconds)", x = "Response") +
  theme_minimal() +
  theme(legend.position="none") +
  ggtitle("RT vs Decider Stimulus")
  #stat_compare_means(label.y = 8.0) +
  #stat_compare_means(ref.group = "Accept", label = "p.signif", label.y = c(7.0))

barplot 

t.test(d$rt[d$decider=="Face"], d$rt[d$decider=="House"])

Accuracy vs Neg/Pos Summed Value

#BAR PLOT
d<- S_M
# Reduce DF to Decider Trials
d$posNeg <- 0
d$posNeg[d$summedVal>0] = "Positive"
d$posNeg[d$summedVal<0] = "Negative"
d <- d[d$summedVal!=0,] # remove values of 0

subject_means <- group_by(d, subject, posNeg) %>%
  dplyr::summarize(accuracy = mean(correct, na.rm = T))
subject_means
subject_means$meanAcc = mean(subject_means$accuracy)

#PLOT
barplot <- ggplot(subject_means, aes(x = posNeg, y = accuracy, fill=posNeg, label=accuracy)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean",
    col = "black",
    stat = "identity"
  ) +
  geom_point(position = position_jitter(h = 0, w = 0.2)) +
  scale_y_continuous(limits = c(0, max(d$accuracy+0.25, na.rm = T)),
                     expand = c(0, 0))+
  labs(y = "Accuracy", x = "Summed Value") +
  theme_minimal() +
  theme(legend.position="none") +
  ggtitle("Accuracy vs Summed Value Sign")
  #stat_compare_means(label.y = 8.0) +
  #stat_compare_means(ref.group = "Accept", label = "p.signif", label.y = c(7.0))

barplot 

t.test(d$correct[d$posNeg=="Positive"], d$correct[d$posNeg=="Negative"])

RT vs Positive/Negative Summed Value

#BAR PLOT
d<- S_M
# Reduce DF to Decider Trials
d$posNeg <- 0
d$posNeg[d$summedVal>0] = "Positive"
d$posNeg[d$summedVal<0] = "Negative"
d <- d[d$summedVal!=0,] # remove values of 0

subject_means <- group_by(d, subject, posNeg) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T))
subject_means

#PLOT
barplot <- ggplot(subject_means, aes(x = posNeg, y = rt, fill=posNeg)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean",
    col = "black",
    stat = "identity"
  ) +
  geom_point(position = position_jitter(h = 0, w = 0.2)) +
  scale_y_continuous(limits = c(0, max(d$rt-2.5, na.rm = T)),
                     expand = c(0, 0))+
  labs(y = "RT (seconds)", x = "Summed Value") +
  theme_minimal() +
  theme(legend.position="none") +
  ggtitle("RT vs Summed Value Sign")
  #stat_compare_means(label.y = 8.0) +
  #stat_compare_means(ref.group = "Accept", label = "p.signif", label.y = c(7.0))

barplot 

t.test(d$rt[d$posNeg=="Positive"], d$rt[d$posNeg=="Negative"])

First/Middle/Last Fixation mean duration (boxPlot)

load("/Users/djw/Dropbox/PROGRAMMING/*NEURO/aDDM_Krajbich/S_M_K.Rdata")
d <- S_M_K

d$fixType <- 2
d$fixType[d$fixNum==1] <- 1
d$fixType[d$revFixNum==1] <-3

median(d$fixDur[d$fixType==1])
median(d$fixDur[d$fixType==2])
median(d$fixDur[d$fixType==3])


my_comparisons <- list( c("1", "2"), c("1", "3"), c("2", "3") )

p0 = ggboxplot(d, x = "fixType", y = "fixDur", color = "fixType", palette = "jco" )+ #outlier.shape=NA
  
  labs(y = "Fixation Time (seconds)", x = "Fixation Type") +
  theme(legend.position="none") +  
  scale_x_discrete(labels=c("1" = "First", "2" = "Middle",
                              "3" = "Last")) +
  #scale_y_continuous(limits = quantile(d$fixDur, c(0.1, 0.9))) +
  stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
  stat_compare_means(label.y = 13) +     # Add global p-value
  ggtitle("Fixation Durations")

p0

## How many trials with more than 2 fixatinos
d = S_M
length(d$Trial[d$swapAmount>2])/length(d$Trial)

mean(d$firstVal[d$swapAmount<2])

Summed Val vs Fixations

df <- S_M

#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=summedVal, y=swapAmount), df) +
  #geom_smooth(aes(x=summedVal, y=logRT, colour = "flip"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  ggtitle("Summed Value vs Fixations")
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  # Add a loess smoothed fit curve with confidence region

#Test for SIG
summary(lm(`2_fixation`~summedVal + factor(secondMult), df))
# create a dummy data frame with outliers
df = data.frame(y = c(-100, rnorm(100), 100))

# create boxplot that includes outliers
p0 = ggplot(df, aes(y = y)) + geom_boxplot(aes(x = factor(1)))

p0
# compute lower and upper whiskers
ylim1 = boxplot.stats(df$y)$stats[c(1, 5)]

# scale y limits based on ylim1
p1 = p0 + coord_cartesian(ylim = ylim1*1.05)
p1

Plot RT with/without Flip

T Test on RT difference between flip and non flip trials RT

library(tidyr)
subject_means_wide <-
  spread(subject_means,
         key = flip,
         value = rt,
         sep = "_")
subject_means_wide

#T-TEST for flip vs. non-flip trials
t.test(subject_means_wide$flip_1, subject_means_wide$flip_2, paired = TRUE)
mean(subject_means_wide$flip_2)
sd(subject_means_wide$flip_2)

Plot performance with/without Flip

There were 50 or more warnings (use warnings() to see the first 50)

Based on the plot it looks like flip trials on average do WORSE and have higher VARIANCE between subjects (in addition to taking longer)

T Test on RT difference between flip and non flip trials % Correct

subject_means_wide <-
  spread(subject_means,
         key = flip,
         value = corPct,
         sep = "_")
subject_means_wide

#T-TEST for flip vs. non-flip trials
t.test(subject_means_wide$flip_1, subject_means_wide$flip_2, paired = TRUE)
sd(subject_means_wide$flip_2)

It turns out that the difference in performance is not significant.

Plot RT vs negative/positive summed Val

Based on the plot it looks like RT is longer for negative summed values.

T Test on RT difference between pos/neg summed values

subject_means_wide <-
  spread(subject_means,
         key = posNegSum,
         value = rt,
         sep = "_")

#T-TEST for flip vs. non-flip trials
t.test(subject_means_wide$posNegSum_0, subject_means_wide$posNegSum_1, paired = TRUE)

This suggests that there is a significantly longer time spent on choices with a negative summed value.

Plot % Correct vs negative/positive summed Val

Based on the plot it looks like people do better when the summed val is positive.

T Test on RT difference between flip and non flip trials % Correct

subject_means_wide <-
  spread(subject_means,
         key = posNegSum,
         value = corPct,
         sep = "_")
subject_means_wide

#T-TEST for flip vs. non-flip trials
t.test(subject_means_wide$posNegSum_0, subject_means_wide$posNegSum_1, paired = TRUE)

The difference is significant. So people take less time but perform better when the summed value is positive.

Multipliers and Absolute Net Value

d <- S_M

# EFFECTS on Abs Val due to MULTS
subject_means <- group_by(d, subject, multNum) %>%
  dplyr::summarize(absNet = mean(absSummedVal, na.rm = T), rt = mean(rt, na.rm = T))
subject_means

# Mean by Mult
mean(subject_means$absNet[subject_means$multNum == 0])
mean(subject_means$absNet[subject_means$multNum == 1])
mean(subject_means$absNet[subject_means$multNum == 2])

# SD by Mult
sd(subject_means$absNet[subject_means$multNum == 0])
sd(subject_means$absNet[subject_means$multNum == 1])
sd(subject_means$absNet[subject_means$multNum == 2])

# 0 1
t.test(subject_means$absNet[subject_means$multNum == 0],
                            subject_means$absNet[subject_means$multNum == 1], paired = TRUE)
# 1 2
t.test(subject_means$absNet[subject_means$multNum == 1],
                            subject_means$absNet[subject_means$multNum == 2], paired = TRUE)
# 0 2
t.test(subject_means$absNet[subject_means$multNum == 0],
       subject_means$absNet[subject_means$multNum == 2], paired = TRUE)

RT distributions for different multNums

d = S_M

med.fac = ddply(d, .(multNum), function(.d)
data.frame(x=median(.d$rt)))

# HISTOGRAM VERSION
p <- ggplot(data = d, aes(x = rt, fill=multNum)) + 
  geom_histogram() +
  labs(title="RT Distribution vs. Number of Multipliers", x = "RT (seconds)", y ="Count") +
  geom_vline(data=med.fac, aes(xintercept=x)) +
  theme_minimal() +
  theme(legend.position="none")
p + facet_wrap(~multNum)

# BOXPLOT VERSION

# create medians to insert as text
x <- d
x$multNum = d$multNum+1
p_meds <- ddply(x, .(multNum), summarise, med = median(rt))
p_meds$med = round(p_meds$med, digits = 2)  # round to two decimal values

# List of conditions to compare
my_comparisons <- list( c("0", "1"), c("0", "2"), c("1", "2") )

p0 = ggboxplot(d, x = "multNum", y = "rt", color = "multNum", palette = "jco" )+ #outlier.shape=NA
  
  labs(y = "Total RT (seconds)", x = "Number of Multipliers") +
  theme(legend.position="none") +  
  scale_x_discrete(labels=c("0" = "None", "1" = "One",
                              "2" = "Two")) +
  #scale_y_continuous(limits = quantile(d$fixDur, c(0.1, 0.9))) +
  stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
  stat_compare_means(label.y = 13) +     # Add global p-value
  geom_text(data = p_meds, aes(x = multNum, y = med, label = med), 
              size = 3, vjust = -1.5) +
  ggtitle("RT Distribution vs. Number of Multipliers")

p0

RT by Mult for “Difficult” trials

# create medians to insert as text
d<- S_M
d <- d[d$absSummedVal<0.5, ] # limit to absolute summed values below 0.50
x <- d
x$multNum = d$multNum+1
p_meds <- ddply(x, .(multNum), summarise, med = median(rt))
p_meds$med = round(p_meds$med, digits = 2)  # round to two decimal values

# List of conditions to compare
my_comparisons <- list( c("0", "1"), c("0", "2"), c("1", "2") )

p0 = ggboxplot(d, x = "multNum", y = "rt", color = "multNum", palette = "jco" )+ #outlier.shape=NA
  
  labs(y = "Total RT (seconds)", x = "Number of Multipliers") +
  theme(legend.position="none") +  
  scale_x_discrete(labels=c("0" = "None", "1" = "One",
                              "2" = "Two")) +
  #scale_y_continuous(limits = quantile(d$fixDur, c(0.1, 0.9))) +
  stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
  stat_compare_means(label.y = 13) +     # Add global p-value
  geom_text(data = p_meds, aes(x = multNum, y = med, label = med), 
              size = 3, vjust = -1.5) +
  ggtitle("RT Distribution vs. Number of Multipliers: Absolute Summed Value < $0.50")

p0

RT by Mult for “Easy” trials

# create medians to insert as text
d<- S_M
d <- d[d$absSummedVal>1.0, ] # limit to absolute summed values below 0.50
x <- d
x$multNum = d$multNum+1
p_meds <- ddply(x, .(multNum), summarise, med = median(rt))
p_meds$med = round(p_meds$med, digits = 2)  # round to two decimal values

# List of conditions to compare
my_comparisons <- list( c("0", "1"), c("0", "2"), c("1", "2") )

p0 = ggboxplot(d, x = "multNum", y = "rt", color = "multNum", palette = "jco" )+ #outlier.shape=NA
  
  labs(y = "Total RT (seconds)", x = "Number of Multipliers") +
  theme(legend.position="none") +  
  scale_x_discrete(labels=c("0" = "None", "1" = "One",
                              "2" = "Two")) +
  #scale_y_continuous(limits = quantile(d$fixDur, c(0.1, 0.9))) +
  stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
  stat_compare_means(label.y = 13) +     # Add global p-value
  geom_text(data = p_meds, aes(x = multNum, y = med, label = med), 
              size = 3, vjust = -1.5) +
  ggtitle("RT Distribution vs. Number of Multipliers: Absolute Summed Value > $1.00")

p0

d<- S_M
d <- d[d$absSummedVal<0.25, ] # limit to absolute summed values below 0.50
subject_means <- group_by(d, multNum) %>%
  dplyr::summarize(median = mean(correct, na.rm = T))
subject_means

What is the mean abs value of combos with 0/1/2 mults?

Highest for 1 multiplier

d <- S_M

d0 <- d[d$multNum == 0, ]
d1 <- d[d$multNum == 1, ]
d2 <- d[d$multNum == 2, ]

mean(d0$absSummedVal)
mean(d1$absSummedVal)
mean(d2$absSummedVal)

PSYCHOMETRICS

Study 1/Study 2, RT and Accuracy

load("Data/NS_M.Rdata")
There were 50 or more warnings (use warnings() to see the first 50)
d1 <- NS_M
d2 <- S_M
# Create ID for each DF
d1$study <- "Standard Mult."
d2$study <- "Swap Mult."
# Need to uniquely number Subjects
d2$subject <- d2$subject + 100
# Concat DFs
common_cols <- intersect(colnames(d1), colnames(d2))
df = rbind(
  d1[, common_cols], 
  d2[, common_cols]
)
# GROUPBY
subject_means <- group_by(df, subject, study) %>%
  dplyr::summarize(accuracy = mean(correct, na.rm = T), rt = mean(rt, na.rm = T), meanSwap = mean(swapAmount, na.rm = T))
Error in summarise_impl(.data, dots) : 
  Evaluation error: object 'swapAmount' not found.

Create Binned Values to test against Accuracy and RT

setwd("/Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots")
The working directory was changed to /Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the the working directory for notebook chunks.
ggsave("Prob.png", width = 19, height = 12, units = "cm")

Same as above but for abs Val for RT and Fixation

  
setwd("/Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots")
The working directory was changed to /Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the the working directory for notebook chunks.
ggsave("RT.png", width = 19, height = 12, units = "cm")

FUNCTIONS for SUMMARY STATS

From: http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_(ggplot2)/#Helper%20functions

## Norms the data within specified groups in a data frame; it normalizes each
## subject (identified by idvar) so that they have the same mean, within each group
## specified by betweenvars.
##   data: a data frame.
##   idvar: the name of a column that identifies each subject (or matched subjects)
##   measurevar: the name of a column that contains the variable to be summariezed
##   betweenvars: a vector containing names of columns that are between-subjects variables
##   na.rm: a boolean that indicates whether to ignore NA's
normDataWithin <- function(data=NULL, idvar, measurevar, betweenvars=NULL,
                           na.rm=FALSE, .drop=TRUE) {
    library(plyr)
    # Measure var on left, idvar + between vars on right of formula.
    data.subjMean <- ddply(data, c(idvar, betweenvars), .drop=.drop,
     .fun = function(xx, col, na.rm) {
        c(subjMean = mean(xx[,col], na.rm=na.rm))
      },
      measurevar,
      na.rm
    )
    # Put the subject means with original data
    data <- merge(data, data.subjMean)
    # Get the normalized data in a new column
    measureNormedVar <- paste(measurevar, "_norm", sep="")
    data[,measureNormedVar] <- data[,measurevar] - data[,"subjMean"] +
                               mean(data[,measurevar], na.rm=na.rm)
    # Remove this subject mean column
    data$subjMean <- NULL
    return(data)
}
## Summarizes data, handling within-subjects variables by removing inter-subject variability.
## It will still work if there are no within-S variables.
## Gives count, un-normed mean, normed mean (with same between-group mean),
##   standard deviation, standard error of the mean, and confidence interval.
## If there are within-subject variables, calculate adjusted values using method from Morey (2008).
##   data: a data frame.
##   measurevar: the name of a column that contains the variable to be summariezed
##   betweenvars: a vector containing names of columns that are between-subjects variables
##   withinvars: a vector containing names of columns that are within-subjects variables
##   idvar: the name of a column that identifies each subject (or matched subjects)
##   na.rm: a boolean that indicates whether to ignore NA's
##   conf.interval: the percent range of the confidence interval (default is 95%)
summarySEwithin <- function(data=NULL, measurevar, betweenvars=NULL, withinvars=NULL,
                            idvar=NULL, na.rm=FALSE, conf.interval=.95, .drop=TRUE) {
  # Ensure that the betweenvars and withinvars are factors
  factorvars <- vapply(data[, c(betweenvars, withinvars), drop=FALSE],
    FUN=is.factor, FUN.VALUE=logical(1))
  if (!all(factorvars)) {
    nonfactorvars <- names(factorvars)[!factorvars]
    message("Automatically converting the following non-factors to factors: ",
            paste(nonfactorvars, collapse = ", "))
    data[nonfactorvars] <- lapply(data[nonfactorvars], factor)
  }
  # Get the means from the un-normed data
  datac <- summarySE(data, measurevar, groupvars=c(betweenvars, withinvars),
                     na.rm=na.rm, conf.interval=conf.interval, .drop=.drop)
  # Drop all the unused columns (these will be calculated with normed data)
  datac$sd <- NULL
  datac$se <- NULL
  datac$ci <- NULL
  # Norm each subject's data
  ndata <- normDataWithin(data, idvar, measurevar, betweenvars, na.rm, .drop=.drop)
  # This is the name of the new column
  measurevar_n <- paste(measurevar, "_norm", sep="")
  # Collapse the normed data - now we can treat between and within vars the same
  ndatac <- summarySE(ndata, measurevar_n, groupvars=c(betweenvars, withinvars),
                      na.rm=na.rm, conf.interval=conf.interval, .drop=.drop)
  # Apply correction from Morey (2008) to the standard error and confidence interval
  #  Get the product of the number of conditions of within-S variables
  nWithinGroups    <- prod(vapply(ndatac[,withinvars, drop=FALSE], FUN=nlevels,
                           FUN.VALUE=numeric(1)))
  correctionFactor <- sqrt( nWithinGroups / (nWithinGroups-1) )
  # Apply the correction factor
  ndatac$sd <- ndatac$sd * correctionFactor
  ndatac$se <- ndatac$se * correctionFactor
  ndatac$ci <- ndatac$ci * correctionFactor
  # Combine the un-normed means with the normed results
  merge(datac, ndatac)
}
## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
##   data: a data frame.
##   measurevar: the name of a column that contains the variable to be summariezed
##   groupvars: a vector containing names of columns that contain grouping variables
##   na.rm: a boolean that indicates whether to ignore NA's
##   conf.interval: the percent range of the confidence interval (default is 95%)
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
                      conf.interval=.95, .drop=TRUE) {
    library(plyr)
    # New version of length which can handle NA's: if na.rm==T, don't count them
    length2 <- function (x, na.rm=FALSE) {
        if (na.rm) sum(!is.na(x))
        else       length(x)
    }
    # This does the summary. For each group's data frame, return a vector with
    # N, mean, and sd
    datac <- ddply(data, groupvars, .drop=.drop,
      .fun = function(xx, col) {
        c(N    = length2(xx[[col]], na.rm=na.rm),
          mean = mean   (xx[[col]], na.rm=na.rm),
          sd   = sd     (xx[[col]], na.rm=na.rm)
        )
      },
      measurevar
    )
    # Rename the "mean" column    
    datac <- rename(datac, c("mean" = measurevar))
    datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean
    # Confidence interval multiplier for standard error
    # Calculate t-statistic for confidence interval: 
    # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
    ciMult <- qt(conf.interval/2 + .5, datac$N-1)
    datac$ci <- datac$se * ciMult
    return(datac)
}

Difficult, Very Difficult, Easy, Overall RT and Accuracy by MultNum

d <- S_M
There were 41 warnings (use warnings() to see them)
# Remove abs summed values >1.00 and <0.50
d <- d[(d$absSummedVal<=0.50) | (d$absSummedVal>=1.00),]
# Create Difficulty Level and Factor it
d$difficulty = 1  # easy level
d$difficulty[d$absSummedVal<0.5] = 2 # Difficult level
d$difficulty[d$absSummedVal<0.25] = 3 # V.Difficult level
# Factor conditions
d$multNum <- factor(d$multNum)
d$difficulty <- factor(d$difficulty)
# FOR T-TESTS
subject_means <- group_by(d, subject, difficulty, multNum) %>%
  dplyr::summarize(accuracy = mean(correct), rt = mean(rt))
subject_means
# Paired TTest
# Accuracy
# Easy vs Hard
mean(subject_means$accuracy[subject_means$difficulty == 1])
[1] 0.9519747
mean(subject_means$accuracy[subject_means$difficulty == 3])
[1] 0.6752014
sd(subject_means$accuracy[subject_means$difficulty == 1])
[1] 0.06967094
sd(subject_means$accuracy[subject_means$difficulty == 3])
[1] 0.1424739
       
t.test(subject_means$accuracy[subject_means$difficulty == 1],
       subject_means$accuracy[subject_means$difficulty == 3], paired = TRUE)

    Paired t-test

data:  subject_means$accuracy[subject_means$difficulty == 1] and subject_means$accuracy[subject_means$difficulty == 3]
t = 14.77, df = 68, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.2393794 0.3141673
sample estimates:
mean of the differences 
              0.2767733 
# Easy, 0 Mult/1 Mult
mean(subject_means$accuracy[subject_means$difficulty == 1 & subject_means$multNum == 0])
[1] 0.9877854
mean(subject_means$accuracy[subject_means$difficulty == 1 & subject_means$multNum == 1])
[1] 0.9482313
sd(subject_means$accuracy[subject_means$difficulty == 1 & subject_means$multNum == 0])
[1] 0.03119811
sd(subject_means$accuracy[subject_means$difficulty == 1 & subject_means$multNum == 1])
[1] 0.05554537
t.test(subject_means$accuracy[subject_means$difficulty == 1 & subject_means$multNum == 0],
       subject_means$accuracy[subject_means$difficulty == 1 & subject_means$multNum == 1], paired = TRUE)

    Paired t-test

data:  subject_means$accuracy[subject_means$difficulty == 1 & subject_means$multNum ==  and subject_means$accuracy[subject_means$difficulty == 1 & subject_means$multNum ==     0] and     1]
t = 5.1382, df = 22, p-value = 3.772e-05
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.02358934 0.05551889
sample estimates:
mean of the differences 
             0.03955411 
# Hard, 0 Mult/1 Mult
mean(subject_means$accuracy[subject_means$difficulty == 3 & subject_means$multNum == 0])
[1] 0.585889
mean(subject_means$accuracy[subject_means$difficulty == 3 & subject_means$multNum == 1])
[1] 0.7318523
sd(subject_means$accuracy[subject_means$difficulty == 3 & subject_means$multNum == 0])
[1] 0.1247233
sd(subject_means$accuracy[subject_means$difficulty == 3 & subject_means$multNum == 1])
[1] 0.1278067
t.test(subject_means$accuracy[subject_means$difficulty == 3 & subject_means$multNum == 0],
       subject_means$accuracy[subject_means$difficulty == 3 & subject_means$multNum == 1], paired = TRUE)

    Paired t-test

data:  subject_means$accuracy[subject_means$difficulty == 3 & subject_means$multNum ==  and subject_means$accuracy[subject_means$difficulty == 3 & subject_means$multNum ==     0] and     1]
t = -4.4311, df = 22, p-value = 0.0002105
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.21427733 -0.07764923
sample estimates:
mean of the differences 
             -0.1459633 
# Paired TTest
# RT
# Easy vs Hard
mean(subject_means$rt[subject_means$difficulty == 1])
[1] 2.714638
mean(subject_means$rt[subject_means$difficulty == 3])
[1] 3.428044
sd(subject_means$rt[subject_means$difficulty == 1])
[1] 0.9481159
sd(subject_means$rt[subject_means$difficulty == 3])
[1] 1.115059
       
t.test(subject_means$rt[subject_means$difficulty == 1],
       subject_means$rt[subject_means$difficulty == 3], paired = TRUE)

    Paired t-test

data:  subject_means$rt[subject_means$difficulty == 1] and subject_means$rt[subject_means$difficulty == 3]
t = -10.001, df = 68, p-value = 5.434e-15
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.8557461 -0.5710664
sample estimates:
mean of the differences 
             -0.7134063 
# Easy, 0 Mult/1 Mult
mean(subject_means$rt[subject_means$difficulty == 1 & subject_means$multNum == 0])
[1] 2.503676
mean(subject_means$rt[subject_means$difficulty == 1 & subject_means$multNum == 1])
[1] 2.666323
sd(subject_means$rt[subject_means$difficulty == 1 & subject_means$multNum == 0])
[1] 0.857097
sd(subject_means$rt[subject_means$difficulty == 1 & subject_means$multNum == 1])
[1] 0.8832649
t.test(subject_means$rt[subject_means$difficulty == 1 & subject_means$multNum == 0],
       subject_means$rt[subject_means$difficulty == 1 & subject_means$multNum == 1], paired = TRUE)

    Paired t-test

data:  subject_means$rt[subject_means$difficulty == 1 & subject_means$multNum ==  and subject_means$rt[subject_means$difficulty == 1 & subject_means$multNum ==     0] and     1]
t = -3.2763, df = 22, p-value = 0.003451
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.2656007 -0.0596928
sample estimates:
mean of the differences 
             -0.1626467 
# Hard, 0 Mult/1 Mult
mean(subject_means$rt[subject_means$difficulty == 3 & subject_means$multNum == 0])
[1] 3.485174
mean(subject_means$rt[subject_means$difficulty == 3 & subject_means$multNum == 1])
[1] 3.216965
sd(subject_means$rt[subject_means$difficulty == 3 & subject_means$multNum == 0])
[1] 1.173273
sd(subject_means$rt[subject_means$difficulty == 3 & subject_means$multNum == 1])
[1] 1.183658
t.test(subject_means$rt[subject_means$difficulty == 3 & subject_means$multNum == 0],
       subject_means$rt[subject_means$difficulty == 3 & subject_means$multNum == 1], paired = TRUE)

    Paired t-test

data:  subject_means$rt[subject_means$difficulty == 3 & subject_means$multNum ==  and subject_means$rt[subject_means$difficulty == 3 & subject_means$multNum ==     0] and     1]
t = 2.3206, df = 22, p-value = 0.02997
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.0285203 0.5078978
sample estimates:
mean of the differences 
              0.2682091 
#------------#
# PLOT       #
#------------#
# For RT
# Stats Summary
datac <- summarySEwithin(d, measurevar="rt", withinvars=c("multNum","difficulty"), idvar="subject")
ggplot(datac, aes(x=difficulty, y=rt, fill=multNum)) +
    geom_bar(position=position_dodge(.9), colour="black", stat="identity") +
    geom_errorbar(position=position_dodge(.9), width=.25, aes(ymin=rt-ci, ymax=rt+ci)) +
    coord_cartesian(ylim=c(2,4)) +
    labs(y = "Total RT (s)", x = "Difficulty (net value)") +
    scale_x_discrete(labels=c("1" = "Easy (>1)", "2" = "Difficult (0.5<0.25)",
                              "3" = "Very Difficult (<0.25)")) +
    scale_y_continuous(breaks=seq(2,4,0.2)) +
    theme_bw() +
    scale_fill_discrete(name="Number of\nMultipliers") 

    #ggtitle("RT vs. Difficulty + Number of Multipliers")  
# For Accuracy
# Stats Summary
datac <- summarySEwithin(d, measurevar="correct", withinvars=c("multNum","difficulty"), idvar="subject")
ggplot(datac, aes(x=difficulty, y=correct, fill=multNum)) +
    geom_bar(position=position_dodge(.9), colour="black", stat="identity") +
    geom_errorbar(position=position_dodge(.9), width=.25, aes(ymin=correct-ci, ymax=correct+ci)) +
    coord_cartesian(ylim=c(0.5,1)) +
    labs(y = "Accuracy", x = "Difficulty (net value)") +
    scale_x_discrete(labels=c("1" = "Easy (>1)", "2" = "Difficult (0.5<0.25)",
                              "3" = "Very Difficult (<0.25)")) +    
    scale_y_continuous(breaks=seq(0,1,0.1)) +
    theme_bw() +
    scale_fill_discrete(name="Number of\nMultipliers") 

    #ggtitle("Accuracy vs. Difficulty + Number of Multipliers")  

ANOVA

ANOVA on difference between multiplier and non multiplier trials RT

Based on this, there is no significant effect (as expressed through RT) in having one multiplier, however there is for having two.

Based on this, there is no significant effect (as expressed through RT) in having one multiplier, however there is for having two.

FIXATION DURATION

load("Data/S_M_K.Rdata")
d <- S_M_K

# Factor conditions
d$subject <- factor(d$subject)

# FOR T-TESTS
subject_means <- group_by(d, subject) %>%
  dplyr::summarize(firstFix = mean(fixDur[fixNum == 1]),
                   middleFix = mean(fixDur[fixNum > 1 & revFixNum > 1]),
                   finalFix = mean(fixDur[revFixNum == 1]))
subject_means

# Paired TTest
# RT
mean(subject_means$firstFix)
mean(subject_means$middleFix)
mean(subject_means$finalFix)
sd(subject_means$firstFix)
sd(subject_means$middleFix)
sd(subject_means$finalFix)

t.test(subject_means$middleFix,
       subject_means$finalFix, paired = TRUE)

Linear Models for Dependent (Fixed) Effects (not taking random effects into account)

First Fixation Duration vs. First Image Total Value

First Fixation Duration vs. First Mult

APRIL 24: NEW ANALYSES

Mixed Models

FOR STARTERS: Does summed value affect RT (using log RT)?

Perhaps unsurprisingly based on what we plotted before, summed value has a highly significant effect on reaction time (controlling for random effects of subjects).

Summed Value as the individual TOTAL values (the value x multiplier) of the FACE and HOUSE

Based on this there is a significant difference between mean RT and rt.model2 as well as between rt.model2 and rt.model1

What about interaction between faceTotal and houseTotal?

So there is a significant interaction bewteen the total house value and the total face value (as expected).

And then what if we look at the components (value * multiplier) of the Total Face and Total House Value?

Again, based on the anova analysis there seems to be significance in the interactions between the values and the multipliers.

—————————————–

RT PLOTS

Plot RT random effects of subjects

Plot RT with multiple lines (average of each subjects line)

  • multiplier trials
  • flip trials
  • non flip trials
  • non mult trials

Note that with the flip trials the summed values ranged from -0.6 to 0.6.

Plot Accuracy (% correct) with multiple lines (average of each subjects line)

  • multiplier trials
  • flip trials
  • non flip trials
  • non mult trials

Same Plot but just for “Difficult” choices

RT for difficult choices by multNum

CHOICE CURVE

% acceptance (sinusoid) -sinusoid for non mult/mult/flip

Value of first item vs. First item fixation time

  • no mult
  • 2x mult
  • 3x mult

Value of second item vs. Second item fixation time

  • no mult
  • 2x mult
  • 3x mult

Summed value vs second item fixation time

Number of swaps

  • based on summed value
  • based on ambiguity of individual stimuli (ie. closer to zero) -stimulus left has ambiguity X, stimulus right has ambiguity Y, summed value has ambiguity Z -how much does individual ambiguity vs combined ambiguity affect RT/swaps

SALIENCY TEST: Mean Fixation House v. Face

People looked at HOUSES longer. More salient? Or more ambiguous?

FINAL FIXATION: Tied to Value?

Are people taking longer for flip trials after accounting for fact that flip trials ALWAYS have multipliers (and non-flip trials don’t)

This is not currently working

Questionnaire Data

Plotting GPA vs. Earnings (unfiltered data):

Stroop Data

ANOVA for significance

#reformat Data Frame
found = which(rt_by_condition!=-999,arr.ind=T)
rtANOVA = data.frame(cbind(found,rt_by_condition[found]))
names(rtANOVA) = c('subj','cond','rt')

rtANOVA$subj = factor(rtANOVA$subj)
rtANOVA$cond = factor(rtANOVA$cond)

myaov = aov(rtANOVA$rt~rtANOVA$cond+Error(rtANOVA$subj))
summary(myaov)

T-Test

#reformat Data Frame
t.test(rt_by_condition$congruent,rt_by_condition$incongruent,paired=T,mu=0,alternative="two.sided",var.equal=T)

Based on Anova/TTest seems like there is a significant difference.

#Select dataframe to use
d <- Stroop.df.clean

#mean RT and Final earnings by subject
Stroop.performance <- group_by(d, subject) %>%
  dplyr::summarize(rt = mean(Response.rt, na.rm = T), accuracy = mean(Response.corr, na.rm = T))
Stroop.performance$performance = Stroop.performance$rt * 1/Stroop.performance$accuracy
#invert so bigger nunbers are better
Stroop.performance$performance = 1/Stroop.performance$performance

Stroop Performance vs. Task Performance

vs Earnings

vs Accuracy

vs RT

EFFECT SIZE

Cohen’s D

#Select dataframe to use
d <- total_M_clean3

#mean RT and Final earnings by subject
rt_mults <- group_by(d, subject) %>%
  dplyr::summarize(mult_0 = mean(RT[multNum==0]), mult_1 = mean(RT[multNum==1], na.rm = T), mult_2 = mean(RT[multNum==2], na.rm=T))
rt_mults

library(lsr)
cohensD(rt_mults$mult_0, rt_mults$mult_1)
cohensD(rt_mults$mult_0, rt_mults$mult_2)

MEANS

List of Mean RTs for Mults

#Select dataframe to use
d <- total_M_clean3

#mean RT depending on multiplier combination
for(i in 1:3){
  for(j in 1:3){
    m <- mean(d$RT[d$mult1House==i & d$mult2Face==j])
    cat(sprintf("House Mult = %s and Face Mult = %s\n", i, j))
    cat(sprintf("Mean: %f\n\n", m))
  }
}

MORE MIXED EFFECTS STUFF

Simulate RTs based on data

Abs Val vs. RT

Subject Level

APA Format Plotting

apatheme=theme_bw()+
  theme(panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        panel.border=element_blank(),
        axis.line=element_line(),
        text=element_text(family='Times'))

LME

with help from Liz

ggplot(fitDf, aes(`Fixed Effects`, z, fill=`Fixed Effects`)) + 
  geom_bar(stat = "identity", width = 0.5) + 
  geom_errorbar(aes(ymin=z-se, ymax=z+se), width=0.4) +
  geom_text(aes(label=star), colour="black", vjust=0, size=6) +
  scale_x_discrete(limits = positions) +
  theme_minimal() +
  theme(axis.title.x=element_text(size=14),
      axis.title.y = element_text(size = 14))+
  theme(legend.position="none")
setwd("/Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots/")
The working directory was changed to /Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the the working directory for notebook chunks.
ggsave("fixedEffects.pdf", width = 20, height = 12, units = "cm")

Test to see if RT for incorrect is longer than RT for correct?

LOOKING AT QUESTIONNAIRE DATA

Note that this is the for the “cleaned” subjects. Running this with all of the subjects gives a significant effect to Self-Control.

MULT DIF and MODELS…

TEST TO SEE IF INTERACTION IS SIGNIFICANT

F-test is used to compare the residual sum of squares of both the models

drop1(model.1i, test = “F”) *doesn’t seem to work with lme (example is with lm)

MODEL VALIDATION

  1. residuals versus fitted values to verify homogeneity

  2. a QQ-plot or histogram of the residuals for normality

  3. residuals versus each explanatory variable to check independence

**Instead of a visual inspection, it is also possible to apply a test for homogeneity. Sokal and Rohlf (1995) describe three such tests, namely 1. the Barlett’s test for homogeneity *sensitive to non-normality! 2. Hartley’s Fmax test and the log-anova 3. Scheffe ́-Box test

help(t.test)
---
title: "Swap_Mult Analysis"
author: "Daniel J Wilson"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

```{r cleanup}
# removes all variables but NOT functions
rm(list = setdiff(ls(), lsf.str()))
```


```{r setup, include=TRUE}
knitr::opts_chunk$set(echo = FALSE)
library(magrittr)
library(ggplot2)
library(dplyr)
library(ggplot2)
library(lme4)
library(tidyr)
library(merTools) 
library(lsr)
library(reshape2)
library(ggpubr)
library(plyr)
library(Hmisc) # cut2
library(RColorBrewer)
```

```{r}
specify_decimal <- function(x, k) trimws(format(round(x, k), nsmall=k))
```

# Image Swap Version

###Analysis of the *MULTIPLIER* trials first
`still need to work out how to select subject if the NM trials based on their performance in the multiplier trials`

##Dataframe header

```{r dataframe}
# set WD
setwd("~/Dropbox/PROGRAMMING/_NEURO/2017_MADE/Analysis")

load("Data/S_M.Rdata")
load("Data/S_M_raw.Rdata")

load("Data/NS_M.Rdata")

load("Data/S_NM.Rdata")
load("Data/S_NM_raw.Rdata")
head(S_M_raw)
```
```{r}
mean(S_NM$rt)
mean(S_NM$accuracy)
mean(S_NM$swapAmount)

mean(S_M$rt)
mean(S_M$accuracy)
sd(S_M$accuracy)
mean(S_M$swapAmount)


subject_means <- group_by(S_M_raw, subject) %>%
  dplyr::summarize(swaps = mean(swapAmount, na.rm =T))
plot(subject_means)

subject_means <- group_by(S_M_raw, subject) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T), finalEarnings = mean(finalEarnings, na.rm = T))
```
## Table of subject count, mean, median, SD, range, skew, kurtosis
### Experiment 1/2
#### Accuracy, rt
##### Uncleaned/Cleaned

```{r}
load("Data/made_descripive_stats.Rdata")
```

## Add all DFs (cleaned), mark by condition
### Plot boxplots of rt and accuracy

```{r}
load("Data/NS_NM.Rdata")
load("Data/NS_M.Rdata")
load("Data/S_NM.Rdata")
load("Data/S_M.Rdata")

NS_NM$type = "NoSwap_NoMult"
NS_M$type = "NoSwap_Mult"
S_NM$type = "Swap_NoMult"
S_M$type = "Swap_Mult"

# Join all DFs
common <- intersect(names(NS_NM), names(NS_M))
df = rbind(NS_NM[,common], NS_M[,common])

common <- intersect(names(S_NM), names(S_M))
df2 = rbind(S_NM[,common], S_M[,common])

common <- intersect(names(df), names(df2))
df_full = rbind(df[,common], df2[,common])

# Boxplot RT
boxplot(rt ~ factor(type),
        varwidth = TRUE, xlab = "Trial Type",
        main = "RT by Trial Type", ylab = "RT in s", data = df_full)

# Barplot of Accuracy
#First get means for each trial condition (type) by Subject
d <- df_full

subject_means <- group_by(d, type) %>%
  dplyr::summarize(accuracy = mean(correct, na.rm = T))

subject_means$accuracy = round(subject_means$accuracy, 3)

#PLOT
lower=c(0.787990, 0.787694, 0.798404, 0.759005) 
upper=c(0.877638, 0.867699, 0.880029, 0.893729)

barplot <- ggplot(subject_means, aes(x = type, y = accuracy)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean",
    col = "black",
    fill = "gray70"
  ) +
  #geom_point(position = position_jitter(h = 0, w = 0.2)) +
  scale_y_continuous(limits = c(0, max(d$correct, na.rm = T)),
                     expand = c(0, 0)) +
  geom_text(aes(label=accuracy), position=position_dodge(width=0.9), vjust=-0.25, hjust=-0.65) +
  geom_errorbar(data=subject_means, mapping=aes(x=type, ymin=upper, ymax=lower), width = 0.3)
barplot + ggtitle("Accuracy by Trial Type")
```


## T-Test NS_M vs S_M accuracy
```{r}
t.test(NS_M$correct, S_M$correct)

t.test(NS_NM$rt[NS_NM$Trial<51], NS_NM$rt[NS_NM$Trial>50])

subject_means <- group_by(S_M_raw, subject) %>%
  dplyr::summarize(swapAvg = mean(swapAvg), finalEarnings = mean(finalEarnings, na.rm = T))
x = subject_means$swapAvg[subject_means$swapAvg>1.6]
length(x)
length(subject_means$subject)
```

## T-test NS vs S RT
```{r}

d1 <- NS_M
d2 <- S_M

# Create ID for each DF
d1$study <- "Standard Mult."
d2$study <- "Swap Mult."

# Need to uniquely number Subjects
d2$subject <- d2$subject + 100

# Concat DFs
common_cols <- intersect(colnames(d1), colnames(d2))
df = rbind(
  d1[, common_cols], 
  d2[, common_cols]
)

# GROUPBY
subject_means <- group_by(df, subject, study) %>%
  dplyr::summarize(accuracy = mean(correct, na.rm = T), rt = mean(rt, na.rm = T))
subject_means

t.test(subject_means$rt[subject_means$study == "Standard Mult."], subject_means$rt[subject_means$study=="Swap Mult."])
```

# Summed Val vs. Accuracy

```{r}
df <- S_M
#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=summedVal, y=correct, group = factor(multNum), colour = factor(multNum)), df) +
  #geom_smooth(aes(x=summedVal, y=correct, colour = "flip"), subset(df, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #ggtitle("Summed Value vs. Accuracy")
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth() +  # Add a loess smoothed fit curve with confidence region
  theme_minimal()+
  scale_x_continuous(name="Net Value ($)", seq(-3,3,0.5), limits = c(-3,3))+
  scale_y_continuous(name = "Accuracy") +
  theme(axis.title.x=element_text(size=18),
      axis.title.y = element_text(size = 18)) +
  scale_colour_brewer(palette="Set2") +
  guides(colour=guide_legend(title="Number of\nRe-Weighted\nAttributes"))

setwd("/Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots/")
ggsave("multVAccuracy.pdf", width = 22, height = 12, units = "cm" )


# Alt versions...trying to understand the difficult/accuracy/multiplier business


# plot 1x1, 2x2, 3x3
dfEqualMult = df[df$mult1House == df$mult2Face, ]
length(dfEqualMult$Trial[dfEqualMult$mult1House == 3])
ggplot() +
  geom_smooth(aes(x=summedVal, y=correct, group = factor(mult1House), colour = factor(mult1House)), dfEqualMult) +
  #geom_smooth(aes(x=summedVal, y=correct, colour = "flip"), subset(df, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #ggtitle("Summed Value vs. Accuracy")
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth() +  # Add a loess smoothed fit curve with confidence region
  theme_minimal()+
  guides(colour=guide_legend("Value of \nMultipliers")) +
  scale_x_continuous(name="Net Value ($)", seq(-3,3,0.5), limits = c(-3,3))+
  scale_y_continuous(name = "Accuracy")

# finesse data
dfEqualMult$difficulty = 1
dfEqualMult$difficulty[abs(dfEqualMult$summedVal) <0.5] = 2
dfEqualMult$difficulty <- factor(dfEqualMult$difficulty)
dfEqualMult$mult1House <- factor(dfEqualMult$mult1House)
# Bar plot equivalent multpliers
datac <- summarySEwithin(dfEqualMult, measurevar="correct", withinvars=c("mult1House","difficulty"), idvar="subject")

ggplot(datac, aes(x=difficulty, y=correct, fill=mult1House)) +
    geom_bar(position=position_dodge(.9), colour="black", stat="identity") +
    geom_errorbar(position=position_dodge(.9), width=.25, aes(ymin=correct-ci, ymax=correct+ci)) +
    coord_cartesian(ylim=c(0.0,1)) +
    labs(y = "Accuracy", x = "Difficulty (net value)") +
    scale_x_discrete(labels=c("1" = "Easy (>=0.5)", "2" = "Difficult (<0.5)")) +    
    scale_y_continuous(breaks=seq(0,1,0.1)) +
    theme_bw() +
    theme(axis.title.x=element_text(size=18),
        axis.title.y = element_text(size = 18)) +
    scale_fill_brewer(palette="Pastel1") +
    guides(fill=guide_legend(title="Value of both\nMultipliers"))

setwd("/Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots")
ggsave("EqualMult.pdf", width = 16, height = 12, units = "cm")

# accuracy vs ambiguity for diffent numbers of multipliers
df$absFaceVal = abs(df$faceVal)
df$absHouseVal = abs(df$houseVal)
df$absTotFaceVal = abs(df$faceTotal)
df$absTotHouseVal = abs(df$houseTotal)

# Remove abs summed values >1.00 and <0.50
dAmb <- df[(df$absFaceVal < 0.5 & df$absHouseVal < 0.5), ]
dNamb <- df[(df$absFaceVal > 0.5 & df$absHouseVal > 0.5), ]

# Create Difficulty Level and Factor it
dAmb$difficulty = 1  # easy level
dAmb$difficulty[dAmb$absSummedVal<0.5] = 2 # Difficult level
dNamb$difficulty = 1  # easy level
dNamb$difficulty[dAmb$absSummedVal<0.5] = 2 # Difficult level
df$difficulty = 1
df$difficulty[abs(df$summedVal) <0.5] = 2

# Factor conditions
dAmb$multNum <- factor(dAmb$multNum)
dAmb$difficulty <- factor(dAmb$difficulty)
dNamb$multNum <- factor(dNamb$multNum)
dNamb$difficulty <- factor(dNamb$difficulty)
df$multNum <- factor(df$multNum)
df$difficulty <- factor(df$difficulty)

dfNoFlip <- df[df$flip==2, ]

# Bar plot number of multipliers
datac <- summarySEwithin(df, measurevar="correct", withinvars=c("multDif","difficulty"), idvar="subject")

ggplot(datac, aes(x=difficulty, y=correct, fill=multDif)) +
    geom_bar(position=position_dodge(.9), colour="black", stat="identity") +
    geom_errorbar(position=position_dodge(.9), width=.25, aes(ymin=correct-ci, ymax=correct+ci)) +
    coord_cartesian(ylim=c(0.0,1)) +
    labs(y = "Accuracy", x = "Difficulty (net value)") +
    scale_x_discrete(labels=c("1" = "Easy (>=0.5)", "2" = "Difficult (<0.5)")) +    
    scale_y_continuous(breaks=seq(0,1,0.1)) +
    theme_bw() +
    theme(axis.title.x=element_text(size=18),
        axis.title.y = element_text(size = 18)) +
    scale_fill_brewer(palette="Pastel2") +
    guides(fill=guide_legend(title="Difference\nbetween\nMultipliers"))

setwd("/Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots")
ggsave("MultDif.pdf", width = 16, height = 12, units = "cm")

# Bar plot difference in multipliers
datac <- summarySEwithin(df, measurevar="correct", withinvars=c("multDif","difficulty"), idvar="subject")

ggplot(datac, aes(x=difficulty, y=correct, fill=multDif)) +
    geom_bar(position=position_dodge(.9), colour="black", stat="identity") +
    geom_errorbar(position=position_dodge(.9), width=.25, aes(ymin=correct-ci, ymax=correct+ci)) +
    coord_cartesian(ylim=c(0.5,1)) +
    labs(y = "Accuracy", x = "Difficulty (net value)") +
    scale_x_discrete(labels=c("1" = "Easy (>1)", "2" = "Difficult (<0.5)")) +    
    scale_y_continuous(breaks=seq(0,1,0.1)) +
    theme_bw() +
    scale_fill_discrete(name="Difference between\nMultipliers") 

# plot dif 0, dif 1, dif 2
ggplot() +
  geom_smooth(aes(x=summedVal, y=correct, group = factor(multDif), colour = factor(multDif)), df) +
  #geom_smooth(aes(x=summedVal, y=correct, colour = "flip"), subset(df, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #ggtitle("Summed Value vs. Accuracy")
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth() +  # Add a loess smoothed fit curve with confidence region
  theme_minimal()+
  guides(colour=guide_legend("Difference between\nMultipliers")) +
  scale_x_continuous(name="Net Value ($)", seq(-3,3,0.5), limits = c(-3,3))+
  scale_y_continuous(name = "Accuracy")

#Test for SIG
summary(lm(correct~summedVal + multNumF + flip, df))
```

# Summed val vs. RT

```{r}
df <- S_M

#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=summedVal, y=rt, group = factor(multNum), colour = factor(multNum)), df) +
  #geom_smooth(aes(x=summedVal, y=rt, colour = "flip"), subset(df, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #ggtitle("RT vs. Summed Val")
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth() +  # Add a loess smoothed fit curve with confidence region
  theme_minimal()+
  guides(colour=guide_legend("Multiplier \nCondition")) +
  scale_x_continuous(name="Net Value ($)", seq(-3,3,0.5), limits = c(-3,3))+
  scale_y_continuous(name = "Reaction Time (s)")
  

#create multnum as factor
df$multNumF = factor(df$multNum)
#Test for SIG
summary(lm(rt~summedVal + multNumF + flip, df))
```

# 

```{r}
df <- S_M

#RT vs. Summed Value First Fixation
ggplot() +
  geom_smooth(aes(x=firstVal, y=`1_fixation`, group = factor(firstMult), colour = factor(firstMult)), df) +
  #geom_smooth(aes(x=summedVal, y=logRT, colour = "flip"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #ggtitle("First Fixation Timing vs Total Value")
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth() +  # Add a loess smoothed fit curve with confidence region
  theme_minimal()+
  guides(colour=guide_legend("Multiplier \nCondition")) +
  scale_x_continuous(name="Attribute Weighted Value ($)", seq(-3,3,0.5), limits = c(-3,3))+
  scale_y_continuous(name = "Attribute Dwell Time (s)")+
  theme(axis.title.x=element_text(size=14),
        axis.title.y = element_text(size = 14))+
  theme(legend.position="none")

#Test for SIG
summary(lm(`1_fixation`~firstVal + factor(firstMult), df))

#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=secondVal, y=`2_fixation`, group = factor(secondMult), colour = factor(secondMult)), df) +
  #geom_smooth(aes(x=summedVal, y=logRT, colour = "flip"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #ggtitle("Second Fixation Timing vs Total Value")
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth() + # Add a loess smoothed fit curve with confidence region
  theme_minimal()+
  guides(colour=guide_legend("Attribute\nWeight")) +
  scale_x_continuous(name="Attribute Weighted Value ($)", seq(-3,3,0.5), limits = c(-3,3))+
  scale_y_continuous(name = "Attribute Dwell Time (s)") +
  theme(axis.title.x=element_text(size=14),
        axis.title.y = element_text(size = 14),
        legend.title = element_text(size = 10))+
  theme(legend.position="none")


#------------------------------#
# Use the Krajich Cleaned Data #
#------------------------------#
# make the data
d<- S_M_K
d$subject <- factor(d$subject) 
# delete all rows but selected
dMid <- d[ which(d$fixNum>1 & d$revFixNum>1), ] # only middle fixations
dMid$currentMult = 0
dMid$currentVal = 0
for( i in 1:length(dMid$trial)){
  if(dMid$roi[i] == 0){
    dMid$currentMult[i] = dMid$multFace[i]
    dMid$currentVal[i] = dMid$totValFace[i]
  }
  if(dMid$roi[i] == 1){
    dMid$currentMult[i] = dMid$multHouse[i]
    dMid$currentVal[i] = dMid$totValHouse[i]
  }
}

dFinal <- d[ which(d$revFixNum==1), ] # only final fixations
dFinal$finalVal = 0
dFinal$finalMult = 0
for (i in 1:length(dFinal$trial)){
  if(dFinal$roi[i] == 0){
    dFinal$finalVal[i] <- dFinal$totValFace[i]
    dFinal$finalMult[i] <- dFinal$multFace[i]
  }
  if(dFinal$roi[i] == 1){
    dFinal$finalVal[i] <- dFinal$totValHouse[i]
    dFinal$finalMult[i] <- dFinal$multHouse[i]
  }
}

#RT vs. Summed Value: Middle Fixations
ggplot() +
  geom_smooth(aes(x=currentVal, y=fixDur, group = factor(currentMult), colour = factor(currentMult)), dMid) +
  #geom_smooth(aes(x=summedVal, y=logRT, colour = "flip"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #ggtitle("Second Fixation Timing vs Total Value")
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth() + # Add a loess smoothed fit curve with confidence region
  theme_minimal()+
  guides(colour=guide_legend("Multiplier \nCondition")) +
  scale_x_continuous(name="Net Value ($)", seq(-3,3,0.5), limits = c(-3,3))+
  scale_y_continuous(name = "Middle Fixation Duration (s)")

#RT vs. Summed Value: Final Fixation
ggplot() +
  geom_smooth(aes(x=finalVal, y=fixDur, group = factor(finalMult), colour = factor(finalMult)), dFinal) +
  #geom_smooth(aes(x=summedVal, y=logRT, colour = "flip"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #ggtitle("Second Fixation Timing vs Total Value")
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth() + # Add a loess smoothed fit curve with confidence region
  theme_minimal()+
  guides(colour=guide_legend("Multiplier \nCondition")) +
  scale_x_continuous(name="Net Value ($)", seq(-3,3,0.5), limits = c(-3,3))+
  scale_y_continuous(name = "Final Fixation Duration (s)")

#Test for SIG
summary(lm(`2_fixation`~secondVal + secondMult, df))

# Save Plot (for SNE)
setwd("/Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots/")
ggsave("2ndFix.pdf", width = 18, height = 12, units = "cm")

```

# Second Fixation vs Summed Value

```{r}
df <- S_M

#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=summedVal, y=`2_fixation`, group = factor(secondMult), colour = factor(secondMult)), df) +
  #geom_smooth(aes(x=summedVal, y=logRT, colour = "flip"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #ggtitle("Second Fixation vs Summed Value")
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  +# Add a loess smoothed fit curve with confidence region
  theme_minimal()+
  guides(colour=guide_legend("Multiplier \nCondition")) +
  scale_x_continuous(name="Trial Net Value ($)", seq(-3,3,0.5), limits = c(-3,3))+
  scale_y_continuous(name = "Fixation Duration (s)")
#Test for SIG
summary(lm(`2_fixation`~summedVal + factor(secondMult), df))
```

# QUESTIONNAIRES 

```{r plot-gpa-effort, echo=FALSE}
load("Data/NS_M.Rdata")
load("Data/S_M_raw.Rdata")

d1 <- NS_M
d2 <- S_M_raw

# Create ID for each DF
d1$study <- "Standard Mult."
d2$study <- "Swap Mult."

d <- S_M_raw

#import Questionnaire data
setwd("~/Dropbox/PHD/CENDRI/Project/Code/LabSharedFolder/MADE01/CODE/GIT/Behavior_Analysis")
Quest.df <- read.csv("csv_files/Questionnaire01_Results.csv")
Quest.df <- Quest.df[(Quest.df$study_version == 2), ]

#mean RT and Final earnings by subject
subject_means <- group_by(d, subject) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T), 
                   finalEarnings = mean(finalEarnings, na.rm = T), 
                   accuracy = mean(correct, na.rm=T))

subject_info <- group_by(Quest.df, subject) %>%
  dplyr::summarize(gpa = mean(GPA, na.rm = T), effort = mean(Effort, na.rm = T), guess = mean(Guessing, na.rm = T), comparative = mean(Compared_to_others, na.rm = T))

subject_means <- merge(subject_means, subject_info, by = "subject")
subject_means

### GGPLOT REGRESSION FUNC if not loaded ##
ggplotRegression <- function (fit) {
  require(ggplot2)
  ggplot(fit$model, aes_string(x=names(fit$model)[2], y=names(fit$model)[1])) +
    geom_point() +
    stat_smooth(method = "lm", col = "red") +
    ggtitle("Testing") +
    labs(title = paste(title, "\n\nAdj R2 = ",signif(summary(fit)$adj.r.squared, 5),
                       "Intercept =",signif(fit$coef[[1]], 5),
                       "Slope =",signif(fit$coef[[2]], 5),
                       "P =",signif(summary(fit)$coef[2,4], 5)))
}

title = "GPA vs Performance"
ggplotRegression(lm(accuracy~gpa, data = subject_means))

ggplotRegression <- function (fit) {
  require(ggplot2)
  ggplot(fit$model, aes_string(x=names(fit$model)[2], y=names(fit$model)[1])) +
    geom_point() +
    stat_smooth(method = "lm", col = "red") +
    scale_x_continuous(name = "Self-Reported Effort", breaks=seq(1,9,1)) +
    scale_y_continuous(name = "Accuracy") +
    
   # ggtitle("Testing") 
    labs(title = paste(title, "\n\nAdj R2 = ",signif(summary(fit)$adj.r.squared, 5),
                       "Intercept =",signif(fit$coef[[1]], 5),
                       "Slope =",signif(fit$coef[[2]], 5),
                       "P =",signif(summary(fit)$coef[2,4], 5)))
}

title = "Self-Reported Effort vs Performance"
ggplotRegression(lm(accuracy~effort, data = subject_means))

subject_means

```


## Boxplot of unfiltered data

You can see which people were not trying:

```{r RT-unfiltered, echo=TRUE}
df <- S_M_raw

# Filter out extremely long rt times
df <- df[!(df$rt>10),]

boxplot(rt ~ factor(subject),
        varwidth = TRUE, xlab = "subject",
        main = "Boxplot of RT conditional on\
        subject", ylab = "RT", data = df)
```


### Plotting mean RT vs. Earnings (unfiltered data):

```{r RT_Earnings-unfiltered, echo=FALSE}

#################
# FUNCTION TO PULL DATA OUT OF LM
#################

ggplotRegression <- function (fit) {
  require(ggplot2)
  ggplot(fit$model, aes_string(x=names(fit$model)[2], y=names(fit$model)[1])) +
    geom_point() +
    stat_smooth(method = "lm", col = "red") +
    ggtitle("Testing") +
    labs(title = paste(title, "\n\nAdj R2 = ",signif(summary(fit)$adj.r.squared, 5),
                       "Intercept =",signif(fit$coef[[1]], 5),
                       "Slope =",signif(fit$coef[[2]], 5),
                       "P =",signif(summary(fit)$coef[2,4], 5)))
}

#################
# INITIAL PLOTS
#################

#Select dataframe to use
d <- S_M_raw
d$correct = as.numeric(d$correct) - 1 #make accuracy numeric

#mean RT and Final earnings by subject
subject_means <- group_by(d, subject) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T), finalEarnings = mean(finalEarnings, na.rm = T))

title = "Earnings as related to Mean RT"
ggplotRegression(lm(finalEarnings~rt, data = subject_means))

#mean RT and Accuracy by subject
subject_means <- group_by(d, subject) %>%
  dplyr::summarize(finalEarnings = mean(finalEarnings, na.rm = T), accuracy = mean(as.numeric(correct), na.rm = T))

title = "Earnings as related to % Correct"
ggplotRegression(lm(finalEarnings~accuracy, data = subject_means))

#mean RT and Final earnings by subject
subject_means2 <- group_by(d, subject) %>%
  dplyr::summarize(flips = mean(swapAmount, na.rm = T), finalEarnings = mean(finalEarnings, na.rm = T))

title = "Earnings as related to Number of Image Swaps"
ggplotRegression(lm(finalEarnings~flips, data = subject_means2))
```



### KMeans to Divide into groups based on swaps/$$ (unfiltered data):

```{r K-Means2, echo=FALSE}
subject_means2 <- group_by(d, subject) %>%
  dplyr::summarize(flips = mean(swapAmount, na.rm = T), finalEarnings = mean(finalEarnings, na.rm = T))

subject_means2$subject = NULL

#Cluster into 2 groups
results <- kmeans(subject_means2, 2)

plot(x = subject_means2$flips, y = subject_means2$finalEarnings,
     col = results$cluster,
     main = "Clustering based on Swaps && Dollars",
     ylab = "Final Earnings",
     xlab = "Swaps")

results
```

Tends to include some people who earned over $50 but never looked at the second image...

```{r K-Means3, echo=FALSE}
#Select dataframe to use
d <- S_M_raw

#mean RT and Final earnings by subject
subject_means2 <- group_by(d, subject) %>%
  dplyr::summarize(accuracy = mean(accuracy, na.rm = T), finalEarnings = mean(finalEarnings, na.rm = T))

#Cluster into 2 groups
subject_means2$subject = NULL

results <- kmeans(subject_means2, 2)

plot(x = subject_means2$accuracy, y = subject_means2$finalEarnings,
     col = results$cluster,
     main = "Clustering based on Accuracy && Earnings",
     ylab = "Final Earnings",
     xlab = "Accuracy")

results
```

### Clean data by removing people with less than 1.5 swap average and less than $0 earnings

```{r Clean-data, echo=TRUE}
#remove earnings below 0
total_M_clean2 <- S_M_raw[!(S_M_raw$finalEarnings<0),]
#remove flip avgs less than 1.5
total_M_clean2 <- total_M_clean2[!(total_M_clean2$flipAvg<1.5),]
length(unique(total_M_clean2$subject))

#MORE AGRESSIVE: REMOVE BELOW 75%
total_M_clean3 <- S_M_raw[!(S_M_raw$accuracy<0.75),]
length(unique(total_M_clean3$subject))
```

Taking out those that swapped images less than 2 times and earned less than $0 leaves *24 subjects*
Taking out those that had an accuracy of below 75% leaves *21 subjects*


## Boxplot of filtered data

A lot of variation in avg. RT and the variance of RTs. 4,7 and 23 seem to have particularly low variance.

```{r RT-filtered, echo=TRUE}
boxplot(rt ~ factor(subject),
        varwidth = TRUE, xlab = "subject",
        main = "Boxplot of RT conditional on\
        subject", ylab = "RT", data = S_M)

```

##Check RT distribution
```{r plot-RTvValue2, echo=FALSE}
#RT
hist(S_M$rt, breaks = 50)
abline(v = mean(S_M$rt),
       col = "royalblue",
       lwd = 2)

#median line
abline(v = median(S_M$rt),
       col = "red",
       lwd = 2)

#LEGEND
legend(x = "topright",
       c(as.expression(bquote(Mean == .(mean(S_M$rt)))), as.expression(bquote(Median == .(median(S_M$rt))))),
       col = c("royalblue", "red"),
       lwd = c(2, 2, 2))
```

Skewed right, as is typical of RT. 

##Histogram of 

```{r plotRTvFrequency-correct, echo=FALSE}
#find subject accruacy (uncleaned)
accuracy = tapply(S_M$correct==1, S_M$subject, mean)

#hists of rt based on congruent and incongruent trials
hist(S_M[S_M$correct==1, ]$rt,
   col=rgb(1,0,0,0.5), breaks=seq(0,10,0.1), ylim=c(0,300), xlab="Reaction Time (s)", main = "")
abline(v=median(S_M[S_M$correct==1, ]$rt), col="red")
hist(S_M[S_M$correct==0, ]$rt,
   col=rgb(0,0,1,0.5), breaks=seq(0,10,0.1), ylim=c(0,300), add=T)
abline(v=median(S_M[S_M$correct==0, ]$rt), col="blue")

legend("bottomright", c("Correct", "Incorrect"), fill=c(rgb(1,0,0,0.5), rgb(0,0,1,0.5)))
legend("topright",
       c(as.expression(bquote(Correct_Median == .(median(S_M[S_M$correct==1, ]$RT)))), as.expression(bquote(Incorrect_Median == .(median(S_M[S_M$correct==0, ]$RT))))),
       col = c("red", "blue"),
       lwd = c(2, 2, 1))
```
## RTs By Subject
```{r fig.width=8,fig.height=16}

g = ggplot(S_M, aes(x = rt)) + geom_histogram() 

g + facet_wrap(~ subject, ncol=2)

```


## Hist of Middle Fixations (All Subjects)
```{r}
df <- S_M

#create version of dataset with only middle fixations (we use this later for drawing random middle fixations)

# make sure every trial ends with an NA val
df$`14_fixation` <- NA     

# find col Number of first fix
# Check that we are starting at column fix#1
firstFix = which(names(df) == "1_fixation")

# find the first NA val in each row
naVal <- vector(mode="numeric", length=0)
for(x in 1:length(df$Trial)){
  naVal[x] <- min(which(is.na(df[x,])))
}

# if naVal >39 then there were at least 3 fixations
midFix <- vector(mode = "numeric", length = 0)
for(x in 1:length(df$Trial)){
  if(naVal[x]>(firstFix+2)){
    for(y in (firstFix+1):(naVal[x]-2)){
      val = df[x,y]
      midFix <- c(midFix, val)
    }
  }
}

mean(midFix)
median(midFix)

S_M$finalFix
hist(midFix,
   col=rgb(1,0,0,0.5), breaks=seq(0,9,0.1), ylim=c(0,900), xlab="RT", main = "Middle Fixations")
#abline(v=median(midFix), col="blue")

legend("topright",
       c(as.expression(bquote(MidFix_Median == .(median(midFix))))),
       col = c("blue"),
       lwd = c(2, 2, 1))

```

## Hist of Middle Fixations by Subject
```{r fig.width=8,fig.height=16}
load("Data/S_M_K.Rdata")
df <- S_M_K

# REMOVE First and Last Fixations
df <- df[(df$fixNum!=1 & df$revFixNum!=1),]

# SUBJECT MEANS AND SD AND FIX #
subject_means <- group_by(df, subject) %>%
  dplyr::summarize(median = median(rt, na.rm = T), mean = mean(rt, na.rm = T), sd = sd(rt, na.rm = T), count = length(rt))
subject_means

# Plot Hist
g = ggplot(df, aes(x = rt)) + 
  geom_histogram(data = transform(df, subject = NULL), fill = "blue", alpha = 0.4) +
  geom_histogram() 

g + facet_wrap(~ subject, ncol=2)

# PLot Density
# "adjust" controls the bandwidth
allFix = transform(df, subject = NULL)

g = ggplot(df, aes(x=rt)) + 
  geom_density(data = allFix, aes(alpha = 0.5, fill = "group")) +
  geom_density(aes(alpha = 0.5, fill = "subject")) +
  #geom_vline(aes(xintercept=mean(rt)), color="black", size=1) +
  scale_fill_manual(name = "Density Plot", 
                    values = c(group = "blue", subject = "red"))

appender <- function(string, prefix = "Subject: ", suffix = "  Mean: ", mean = specify_decimal(subject_means$mean[subject_means$subject == (as.numeric(string))],2)) paste0(prefix, string, suffix, mean)
              
g + facet_wrap(~ subject, ncol=2, labeller = as_labeller(appender)) +
  theme(strip.text = element_text(size = 22),
        axis.text.x = element_text(size = 18),
        axis.title.x = element_text(size = 25),
        axis.text.y = element_text(size = 18),
        axis.title.y = element_text(size = 25),
        legend.text = element_text(size = 18),
        legend.title = element_text(size = 21)) +
  ggtitle("Subject vs Group Middle Fixations (in s)")


```


##Log RT histogram
```{r plot-RTvValue, echo=FALSE}
#RT

hist(log(total_M_clean3$RT), breaks = 50)
abline(v = mean(log(total_M_clean3$RT)),
       col = "royalblue",
       lwd = 2)

#median line
abline(v = median(log(total_M_clean3$RT)),
       col = "red",
       lwd = 2)

#LEGEND
legend(x = "topright",
       c(as.expression(bquote(Mean == .(mean(log(total_M_clean3$RT))))), as.expression(bquote(Median == .(median(log(total_M_clean3$RT)))))),
       col = c("royalblue", "red"),
       lwd = c(2, 2, 2))
```


##Plot RT vs Summed Value 
```{r plot-rtVal2, echo=FALSE}
d <- S_M

#RT vs. Summed Value
ggplot(d, aes(x=summedVal, y=rt)) +
  geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  # Add a loess smoothed fit curve with confidence region
```

##Plot LOG RT vs Summed Value 
```{r plot-log-rtVal1, echo=FALSE}

#RT vs. Summed Value
ggplot(total_M_clean3, aes(x=summedVal, y=logRT)) +
  geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  # Add a loess smoothed fit curve with confidence region
```

##Plot Accuracy vs Difficulty
```{r plot-accuracyDifficulty, echo=FALSE}
#DELETE THIS
#total_M_clean3$absSummedVal <- abs(total_M_clean3$summedVal)

#Kernal Density of abs(SummedVal)
d <- density(total_M_clean3$absSummedVal)
plot(d, main = "Kernel Density of Combined Value")

title = "Accuracy vs. Difficulty"
ggplotRegression(lm(correct~absDiff, data = total_M_clean3))
```

##Hist of Accuracy vs Difficulty
```{r hist-accuracyDifficulty, echo=FALSE}

d <- S_M
#d$bins <- cut(d$absSummedVal, breaks = 10, dig.lab = 2)
d$bins <- cut(d$absSummedVal, seq(from=0, to=6, by=0.5), dig.lab = 2)

#qplot(bins, correct, data = d, stat = "summary", fun.y = "mean")

m <- tapply(d$correct, d$bins, mean)
sd <- tapply(d$correct, d$bins, sd)
df <- data.frame(mean.y = m, sd = sd, bin = names(m))
# Points:
#ggplot(df, aes(x=bin, y=mean.y,
#              ymin = mean.y - 1.96*sd,
#              ymax = mean.y + 1.96*sd)) +
#  geom_errorbar() + geom_point(size=3)

ggplot(df, aes(x=bin, y=mean.y)) +
  labs(x = "absSummedVal", y = "Mean Accuracy Rate", title = "Accuracy vs Difficulty") +
  geom_point(size=3)
```

## SIGMOID of ACCEPT VS VALUE

```{r}
d <- S_M

plot(d$summedVal, d$choice,
     main = "Choice vs. Summed Val",
     xlab="Summed Val", ylab="P (Accept)",
     xlim=c(-3, 3))

model <- glm(choice ~ summedVal, data=d, family=binomial(link = logit))
summary(model)

xv <- seq(min(d$summedVal), max(d$summedVal), 0.01)
yv <- predict(model,list(summedVal=xv), type="response")

abline(0.5,0, lty=2)
lines(xv,yv)

#Find the inflection point where there is a 50/50 probability of subject accepting.
p <- 0.5
x <- (log(p/(1-p)) - coef(model)[1]) / coef(model)[2]
x
```

## Final Fix vs Choice

#TWO DIFFICULTY CONDITIONS (Hard/Easy)
##Plot Hard vs. Easy for Subjects
```{r flip-barplot5, echo=FALSE}
#DELETE Create Difficulty Column
#DELETE total_M_clean3$difficulty <- 0;
#DELETE total_M_clean3$difficulty[total_M_clean3$absSummedVal<0.5] = 1;

#BAR PLOT
#First get means for each condition of FLIP by Subject
d <- total_M_clean3

subject_means <- group_by(d, subject, difficulty) %>%
  dplyr::summarize(accuracy = mean(correct, na.rm = T))

#PLOT
barplot <- ggplot(subject_means, aes(x = difficulty, y = accuracy)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean",
    col = "black",
    fill = "gray70"
  ) +
  geom_point(position = position_jitter(h = 0, w = 0.2)) +
  scale_y_continuous(limits = c(0, 1.2),
                     expand = c(0, 0)) +
  labs(title = "Accuracy vs Difficulty", subtitle = "0.0 = absolute summed value of images < $0.50 ")
barplot

#T.Test Means of Subjects
t.test(subject_means$accuracy[subject_means$difficulty==0], subject_means$accuracy[subject_means$difficulty==1])
```


##Plot LOG RT vs Absolute Summed Value 
```{r plot-log-rtVal3, echo=FALSE}
total_M_clean3$absVal = abs(total_M_clean3$summedVal)
  
#RT vs. Summed Value
ggplot(total_M_clean3, aes(x=absVal, y=logRT)) +
  geom_point(shape=1) +    # Use hollow circles
  coord_cartesian(xlim = c(0, 3))  +
  geom_smooth()  # Add a loess smoothed fit curve with confidence region

#Test for Sig
summary(lm(logRT~absVal, total_M_clean3))
```

##Plot Image Swaps vs Summed Value 
```{r plot-rtVal3, echo=FALSE}
#rename column to make more clear
total_M_clean3$swapCount <- total_M_clean3$flipAmount
#RT vs. Summed Value
ggplot(total_M_clean3, aes(x=summedVal, y=swapCount)) +
  geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  # Add a loess smoothed fit curve with confidence region
```


##Plot Image Swaps vs Absolute Value 
```{r plot-rtVal4, echo=FALSE}
#RT vs. Summed Value
ggplot(total_M_clean3, aes(x=absSummedVal, y=swapAmount)) +
  geom_point(shape=1) 
```

##Mean Number of Fixations
```{r plot-fixationNum01, echo=FALSE}

d <- total_M_clean3

subject_means <- group_by(d, subject) %>%
  dplyr::summarize(swapAvg = mean(swapAmount, na.rm = T))

#PLOT

#Mean Number of Swaps
mean(d$swapAmount)

count = seq(1,13)
fixation <- data.frame(count)

for(fixNum in 1:13){
  fixation$means[fixNum] <-mean(total_M_clean3[[paste0(fixNum, "_fixation")]], na.rm=TRUE)
  fixation$sd[fixNum] <-sd(total_M_clean3[[paste0(fixNum, "_fixation")]], na.rm=TRUE)
  fixation$count[fixNum] <- length(which(!is.na(total_M_clean3[[paste0(fixNum, "_fixation")]])))
}

t.test(total_M_clean3$`2_fixation`, total_M_clean3$`3_fixation`, na.action=na.omit)

# Last Fix Time
for(x in 1:length(total_M_clean3$Trial)){
  total_M_clean3$lastFixTime[x] <- total_M_clean3[[paste0(total_M_clean3$swapAmount[x], "_fixation")]][x]
  # Last image times
  if(total_M_clean3$lastImage[x] == 0){   #if last image = 0 then faceVal
    total_M_clean3$lastFixVal[x] = total_M_clean3$faceTotal[x]    
  }
  else{
    total_M_clean3$lastFixVal[x] = total_M_clean3$houseTotal[x]    
  }
}

mean(total_M_clean3$lastImage)
mean(total_M_clean3$lastFixVal)
mean(total_M_clean3$lastFixTime, na.rm=T)

length(total_M_clean3$Trial)

# strangely, two NA values (seem to think there was an extra swap)
which(is.na(total_M_clean3$lastFixTime))
```


##Summed Value vs. Accept/Reject Decision
###Logistic regression curve
```{r plot-new, echo=FALSE}
#This was run to find a weird value in the column
unique(total_M_clean3$acceptReject, incomparables = FALSE)
total_M_clean3$acceptReject[5040][[1]] = 0

#convert to double from LIST
total_M_clean3$acceptReject <- as.numeric(unlist(total_M_clean3$acceptReject))

plot(total_M_clean3$summedVal, total_M_clean3$acceptReject)

model <- glm(acceptReject ~ summedVal, data=total_M_clean3, family=binomial(link = logit))
summary(model)

xv <- seq(min(S_M_raw$summedVal), max(S_M_raw$summedVal), 0.01)
yv <- predict(model,list(summedVal=xv), type="response")

lines(xv,yv)

#Find the inflection point where there is a 50/50 probability of subject accepting.
p <- 0.5
x <- (log(p/(1-p)) - coef(model)[1]) / coef(model)[2]
x
```

## RT Correct vs Incorrect Responses
```{r}
#GOOD BASE FOR PLOTTING EXAMPLE

#BAR PLOT
d <- S_M
d$correct[d$correct==1] = "Correct"
d$correct[d$correct==0] = "Incorrect"

subject_means <- group_by(d, subject, correct) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T))
subject_means

#PLOT
barplot <- ggplot(subject_means, aes(x = correct, y = rt, fill=correct)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean",
    col = "black",
    stat = "identity"
  ) +
  geom_point(position = position_jitter(h = 0, w = 0.2)) +
  scale_y_continuous(limits = c(0, max(d$rt, na.rm = T)),
                     expand = c(0, 0))+
  labs(y = "RT (seconds)", x = "Response")+
  theme_minimal()+
  theme(legend.position="none") +
  ggtitle("RT vs Correct Choice")

barplot 
t.test(d$rt[d$correct=="Correct"], d$rt[d$correct=="Incorrect"])
```

## BarPlot for Choice vs RT
```{r}
#BAR PLOT
d<- S_M
d$choice[d$choice==1] = "Accept"
d$choice[d$choice==0] = "Reject"

subject_means <- group_by(d, subject, choice) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T))
subject_means

#PLOT
barplot <- ggplot(subject_means, aes(x = choice, y = rt, fill=choice)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean",
    col = "black",
    stat = "identity"
  ) +
  geom_point(position = position_jitter(h = 0, w = 0.2)) +
  scale_y_continuous(limits = c(0, max(d$rt, na.rm = T)),
                     expand = c(0, 0))+
  labs(y = "RT (seconds)", x = "Response") +
  theme_minimal() +
  theme(legend.position="none") +
  ggtitle("RT vs Choice")
  #stat_compare_means(label.y = 8.0) +
  #stat_compare_means(ref.group = "Accept", label = "p.signif", label.y = c(7.0))

barplot 

t.test(d$rt[d$choice=="Accept"], d$rt[d$choice=="Reject"])
```

## Deciding Factor: House/Face

```{r}
#BAR PLOT
d<- S_M
# Reduce DF to Decider Trials
x<- (d$faceTotal>0 & d$houseTotal<0) | (d$faceTotal<0 & d$houseTotal>0)
d = d[x==TRUE,]
# Create Decider Column
d$decider = 0
d$decider[abs(d$faceTotal)>abs(d$houseTotal)] = "Face"
d$decider[abs(d$houseTotal)>abs(d$faceTotal)] = "House"
#remove values of zero
d = d[d$decider!=0,]

subject_means <- group_by(d, subject, decider) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T))
subject_means

#PLOT
barplot <- ggplot(subject_means, aes(x = decider, y = rt, fill=decider)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean",
    col = "black",
    stat = "identity"
  ) +
  geom_point(position = position_jitter(h = 0, w = 0.2)) +
  scale_y_continuous(limits = c(0, max(d$rt, na.rm = T)),
                     expand = c(0, 0))+
  labs(y = "RT (seconds)", x = "Response") +
  theme_minimal() +
  theme(legend.position="none") +
  ggtitle("RT vs Decider Stimulus")
  #stat_compare_means(label.y = 8.0) +
  #stat_compare_means(ref.group = "Accept", label = "p.signif", label.y = c(7.0))

barplot 

t.test(d$rt[d$decider=="Face"], d$rt[d$decider=="House"])
```

## Accuracy vs Neg/Pos Summed Value

```{r}
#BAR PLOT
d<- S_M
# Reduce DF to Decider Trials
d$posNeg <- 0
d$posNeg[d$summedVal>0] = "Positive"
d$posNeg[d$summedVal<0] = "Negative"
d <- d[d$summedVal!=0,] # remove values of 0

subject_means <- group_by(d, subject, posNeg) %>%
  dplyr::summarize(accuracy = mean(correct, na.rm = T))
subject_means
subject_means$meanAcc = mean(subject_means$accuracy)

#PLOT
barplot <- ggplot(subject_means, aes(x = posNeg, y = accuracy, fill=posNeg, label=accuracy)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean",
    col = "black",
    stat = "identity"
  ) +
  geom_point(position = position_jitter(h = 0, w = 0.2)) +
  scale_y_continuous(limits = c(0, max(d$accuracy+0.25, na.rm = T)),
                     expand = c(0, 0))+
  labs(y = "Accuracy", x = "Summed Value") +
  theme_minimal() +
  theme(legend.position="none") +
  ggtitle("Accuracy vs Summed Value Sign")
  #stat_compare_means(label.y = 8.0) +
  #stat_compare_means(ref.group = "Accept", label = "p.signif", label.y = c(7.0))

barplot 

t.test(d$correct[d$posNeg=="Positive"], d$correct[d$posNeg=="Negative"])
```

## RT vs Positive/Negative Summed Value

```{r}
#BAR PLOT
d<- S_M
# Reduce DF to Decider Trials
d$posNeg <- 0
d$posNeg[d$summedVal>0] = "Positive"
d$posNeg[d$summedVal<0] = "Negative"
d <- d[d$summedVal!=0,] # remove values of 0

subject_means <- group_by(d, subject, posNeg) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T))
subject_means

#PLOT
barplot <- ggplot(subject_means, aes(x = posNeg, y = rt, fill=posNeg)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean",
    col = "black",
    stat = "identity"
  ) +
  geom_point(position = position_jitter(h = 0, w = 0.2)) +
  scale_y_continuous(limits = c(0, max(d$rt-2.5, na.rm = T)),
                     expand = c(0, 0))+
  labs(y = "RT (seconds)", x = "Summed Value") +
  theme_minimal() +
  theme(legend.position="none") +
  ggtitle("RT vs Summed Value Sign")
  #stat_compare_means(label.y = 8.0) +
  #stat_compare_means(ref.group = "Accept", label = "p.signif", label.y = c(7.0))

barplot 

t.test(d$rt[d$posNeg=="Positive"], d$rt[d$posNeg=="Negative"])
```

## First/Middle/Last Fixation mean duration (boxPlot)

```{r}
load("/Users/djw/Dropbox/PROGRAMMING/*NEURO/aDDM_Krajbich/S_M_K.Rdata")
d <- S_M_K

d$fixType <- 2
d$fixType[d$fixNum==1] <- 1
d$fixType[d$revFixNum==1] <-3

median(d$fixDur[d$fixType==1])
median(d$fixDur[d$fixType==2])
median(d$fixDur[d$fixType==3])


my_comparisons <- list( c("1", "2"), c("1", "3"), c("2", "3") )

p0 = ggboxplot(d, x = "fixType", y = "fixDur", color = "fixType", palette = "jco" )+ #outlier.shape=NA
  
  labs(y = "Fixation Time (seconds)", x = "Fixation Type") +
  theme(legend.position="none") +  
  scale_x_discrete(labels=c("1" = "First", "2" = "Middle",
                              "3" = "Last")) +
  #scale_y_continuous(limits = quantile(d$fixDur, c(0.1, 0.9))) +
  stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
  stat_compare_means(label.y = 13) +     # Add global p-value
  ggtitle("Fixation Durations")

p0

## How many trials with more than 2 fixatinos
d = S_M
length(d$Trial[d$swapAmount>2])/length(d$Trial)

mean(d$firstVal[d$swapAmount<2])
```

## Summed Val vs Fixations

```{r}
df <- S_M

#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=summedVal, y=swapAmount), df) +
  #geom_smooth(aes(x=summedVal, y=logRT, colour = "flip"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  ggtitle("Summed Value vs Fixations")
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  # Add a loess smoothed fit curve with confidence region

#Test for SIG
summary(lm(`2_fixation`~summedVal + factor(secondMult), df))
```


```{r}
# create a dummy data frame with outliers
df = data.frame(y = c(-100, rnorm(100), 100))

# create boxplot that includes outliers
p0 = ggplot(df, aes(y = y)) + geom_boxplot(aes(x = factor(1)))

p0
# compute lower and upper whiskers
ylim1 = boxplot.stats(df$y)$stats[c(1, 5)]

# scale y limits based on ylim1
p1 = p0 + coord_cartesian(ylim = ylim1*1.05)
p1
```


##Plot RT with/without Flip
```{r flip-barplot2, echo=FALSE}

#BAR PLOT
#First get means for each condition of FLIP by Subject
d <- S_M

d$flip <- factor(d$flip)

subject_means <- group_by(d, subject, flip) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T))

collapsedData = data.frame("Trial" = c(1,2), "Mean" = 0, "SE" = 0)
collapsedData$Mean[collapsedData$Trial == 1] = mean(subject_means$rt[subject_means$flip == 1])
collapsedData$Mean[collapsedData$Trial == 2] = mean(subject_means$rt[subject_means$flip == 2])
collapsedData$SE[collapsedData$Trial == 1] = sd(subject_means$rt[subject_means$flip == 1])/sqrt(length(unique(subject_means$subject)))
collapsedData$SE[collapsedData$Trial == 2] = sd(subject_means$rt[subject_means$flip == 2])/sqrt(length(unique(subject_means$subject)))

#PLOT
barplot <- ggplot(subject_means, aes(x = flip, y = rt, fill=flip)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean"
  ) +
  geom_point(position = position_jitter(h = 0, w = 0.2)) +
  labs(y = "Reaction Time (seconds)", x = "Trial Type") +
  scale_x_discrete(labels=c("1" = "Reversal", "2" = "Non-Reversal")) +  
  scale_y_continuous(limits = c(0, max(d$rt, na.rm = T) - 3),
                     expand = c(0, 0)) + 
  theme_minimal() +
  theme(legend.position="none")
barplot

# DOn't plot individudal subject, add SE bars

collapsedData$Trial <- factor(collapsedData$Trial)
p<- ggplot(collapsedData, aes(x=Trial, y=Mean, fill=Trial)) + 
  geom_bar(stat="identity", color="black", 
           position=position_dodge()) +
  geom_errorbar(aes(ymin=Mean-SE, ymax=Mean+SE), width=.2,
                 position=position_dodge(.9)) +
  labs(y = "Reaction Time (s)", x = "Trial Type") +
  scale_x_discrete(labels=c("1" = "Reversal", "2" = "Non-Reversal")) +
  coord_cartesian(ylim=c(2.2, 3.5)) +
  theme_minimal() +
  theme(legend.position="none")
p
```

##T Test on RT difference between flip and non flip trials RT
```{r t-test, echo=TRUE}
library(tidyr)
subject_means_wide <-
  spread(subject_means,
         key = flip,
         value = rt,
         sep = "_")
subject_means_wide

#T-TEST for flip vs. non-flip trials
t.test(subject_means_wide$flip_1, subject_means_wide$flip_2, paired = TRUE)
mean(subject_means_wide$flip_2)
sd(subject_means_wide$flip_2)
```

##Plot performance with/without Flip
```{r flip-barplot, echo=FALSE}

#BAR PLOT
#First get means for each condition of FLIP by Subject
d <- S_M

d$flip <- factor(d$flip)

subject_means <- group_by(d, subject, flip) %>%
  dplyr::summarize(corPct = mean(correct, na.rm = T))

collapsedData = data.frame("Trial" = c(1,2), "Mean" = 0, "SE" = 0)
collapsedData$Mean[collapsedData$Trial == 1] = mean(subject_means$corPct[subject_means$flip == 1])
collapsedData$Mean[collapsedData$Trial == 2] = mean(subject_means$corPct[subject_means$flip == 2])
collapsedData$SE[collapsedData$Trial == 1] = sd(subject_means$corPct[subject_means$flip == 1])/sqrt(length(unique(subject_means$subject)))
collapsedData$SE[collapsedData$Trial == 2] = sd(subject_means$corPct[subject_means$flip == 2])/sqrt(length(unique(subject_means$subject)))

#PLOT
barplot <- ggplot(subject_means, aes(x = flip, y = corPct, fill = flip)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean"
  ) +
  geom_point(position = position_jitter(h = 0, w = 0.2)) +
  labs(y = "p(Correct)", x = "Trial Type") +
  scale_x_discrete(labels=c("1" = "Reversal", "2" = "Non-Reversal")) +  
  scale_y_continuous(limits = c(0, max(d$correct, na.rm = T)+.1),
                     expand = c(0, 0))+
  theme_minimal() +
  #geom_errorbar(aes(ymin=corPct-sd(corPct), ymax=corPct+sd(corPct)), width=.1) +
  theme(legend.position="none")
barplot

collapsedData$Trial <- factor(collapsedData$Trial)
p<- ggplot(collapsedData, aes(x=Trial, y=Mean, fill=Trial)) + 
  geom_bar(stat="identity", color="black", 
           position=position_dodge()) +
  geom_errorbar(aes(ymin=Mean-SE, ymax=Mean+SE), width=.2,
                 position=position_dodge(.9)) +
  labs(y = "p(Correct)", x = "Trial Type") +
  scale_x_discrete(labels=c("1" = "Reversal", "2" = "Non-Reversal")) +
  coord_cartesian(ylim=c(0.77, 0.88)) +
  theme_minimal() +
  theme(legend.position="none")
p
```

Based on the plot it looks like flip trials on average do *WORSE* and have higher *VARIANCE* between subjects (in addition to taking longer)

##T Test on RT difference between flip and non flip trials % Correct
```{r t-test2, echo=TRUE}
subject_means_wide <-
  spread(subject_means,
         key = flip,
         value = corPct,
         sep = "_")
subject_means_wide

#T-TEST for flip vs. non-flip trials
t.test(subject_means_wide$flip_1, subject_means_wide$flip_2, paired = TRUE)
sd(subject_means_wide$flip_2)
```

It turns out that the difference in performance is *not* significant.



##Plot RT vs negative/positive summed Val
```{r posNeg-barplot2, echo=FALSE}

#BAR PLOT
#First get means for each condition of FLIP by Subject
d <- total_M_clean3

d$posNegSum <- 0
d$posNegSum[d$summedVal>0] <- 1

subject_means <- group_by(d, subject, posNegSum) %>%
  dplyr::summarize(rt = mean(RT, na.rm = T))
subject_means

#PLOT
barplot <- ggplot(subject_means, aes(x = posNegSum, y = rt)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean",
    col = "black",
    fill = "gray70"
  ) +
  geom_point(position = position_jitter(h = 0, w = 0.2)) +
  scale_y_continuous(limits = c(0, max(d$RT, na.rm = T)),
                     expand = c(0, 0))
barplot
```

Based on the plot it looks like RT is *longer* for negative summed values.

##T Test on RT difference between pos/neg summed values
```{r t-test4, echo=TRUE}
subject_means_wide <-
  spread(subject_means,
         key = posNegSum,
         value = rt,
         sep = "_")

#T-TEST for flip vs. non-flip trials
t.test(subject_means_wide$posNegSum_0, subject_means_wide$posNegSum_1, paired = TRUE)
```

This suggests that there is a significantly longer time spent on choices with a negative summed value.


##Plot % Correct vs negative/positive summed Val
```{r posNeg-barplot, echo=FALSE}

#BAR PLOT
#First get means for each condition of FLIP by Subject
d <- total_M_clean3

d$posNegSum <- 0
d$posNegSum[d$summedVal>0] <- 1

subject_means <- group_by(d, subject, posNegSum) %>%
  dplyr::summarize(corPct = mean(correct, na.rm = T))

#PLOT
barplot <- ggplot(subject_means, aes(x = posNegSum, y = corPct)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean",
    col = "black",
    fill = "gray70"
  ) +
  geom_point(position = position_jitter(h = 0, w = 0.2)) +
  scale_y_continuous(limits = c(0, max(d$correct, na.rm = T)),
                     expand = c(0, 0))
barplot
```

Based on the plot it looks like people do *better* when the summed val is positive.

##T Test on RT difference between flip and non flip trials % Correct
```{r t-test3, echo=TRUE}
subject_means_wide <-
  spread(subject_means,
         key = posNegSum,
         value = corPct,
         sep = "_")
subject_means_wide

#T-TEST for flip vs. non-flip trials
t.test(subject_means_wide$posNegSum_0, subject_means_wide$posNegSum_1, paired = TRUE)
```

The difference is significant. So people take less time but perform better when the summed value is positive.

## Multipliers and Absolute Net Value

```{r}
d <- S_M

# EFFECTS on Abs Val due to MULTS
subject_means <- group_by(d, subject, multNum) %>%
  dplyr::summarize(absNet = mean(absSummedVal, na.rm = T), rt = mean(rt, na.rm = T))
subject_means

# Mean by Mult
mean(subject_means$absNet[subject_means$multNum == 0])
mean(subject_means$absNet[subject_means$multNum == 1])
mean(subject_means$absNet[subject_means$multNum == 2])

# SD by Mult
sd(subject_means$absNet[subject_means$multNum == 0])
sd(subject_means$absNet[subject_means$multNum == 1])
sd(subject_means$absNet[subject_means$multNum == 2])

# 0 1
t.test(subject_means$absNet[subject_means$multNum == 0],
                            subject_means$absNet[subject_means$multNum == 1], paired = TRUE)
# 1 2
t.test(subject_means$absNet[subject_means$multNum == 1],
                            subject_means$absNet[subject_means$multNum == 2], paired = TRUE)
# 0 2
t.test(subject_means$absNet[subject_means$multNum == 0],
       subject_means$absNet[subject_means$multNum == 2], paired = TRUE)

```


## RT distributions for different multNums

```{r}
d = S_M

med.fac = ddply(d, .(multNum), function(.d)
data.frame(x=median(.d$rt)))

# HISTOGRAM VERSION
p <- ggplot(data = d, aes(x = rt, fill=multNum)) + 
  geom_histogram() +
  labs(title="RT Distribution vs. Number of Multipliers", x = "RT (seconds)", y ="Count") +
  geom_vline(data=med.fac, aes(xintercept=x)) +
  theme_minimal() +
  theme(legend.position="none")
p + facet_wrap(~multNum)

# BOXPLOT VERSION

# create medians to insert as text
x <- d
x$multNum = d$multNum+1
p_meds <- ddply(x, .(multNum), summarise, med = median(rt))
p_meds$med = round(p_meds$med, digits = 2)  # round to two decimal values

# List of conditions to compare
my_comparisons <- list( c("0", "1"), c("0", "2"), c("1", "2") )

p0 = ggboxplot(d, x = "multNum", y = "rt", color = "multNum", palette = "jco" )+ #outlier.shape=NA
  
  labs(y = "Total RT (seconds)", x = "Number of Multipliers") +
  theme(legend.position="none") +  
  scale_x_discrete(labels=c("0" = "None", "1" = "One",
                              "2" = "Two")) +
  #scale_y_continuous(limits = quantile(d$fixDur, c(0.1, 0.9))) +
  stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
  stat_compare_means(label.y = 13) +     # Add global p-value
  geom_text(data = p_meds, aes(x = multNum, y = med, label = med), 
              size = 3, vjust = -1.5) +
  ggtitle("RT Distribution vs. Number of Multipliers")

p0
```

## RT by Mult for "Difficult" trials
```{r}
# create medians to insert as text
d<- S_M
d <- d[d$absSummedVal<0.5, ] # limit to absolute summed values below 0.50
x <- d
x$multNum = d$multNum+1
p_meds <- ddply(x, .(multNum), summarise, med = median(rt))
p_meds$med = round(p_meds$med, digits = 2)  # round to two decimal values

# List of conditions to compare
my_comparisons <- list( c("0", "1"), c("0", "2"), c("1", "2") )

p0 = ggboxplot(d, x = "multNum", y = "rt", color = "multNum", palette = "jco" )+ #outlier.shape=NA
  
  labs(y = "Total RT (seconds)", x = "Number of Multipliers") +
  theme(legend.position="none") +  
  scale_x_discrete(labels=c("0" = "None", "1" = "One",
                              "2" = "Two")) +
  #scale_y_continuous(limits = quantile(d$fixDur, c(0.1, 0.9))) +
  stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
  stat_compare_means(label.y = 13) +     # Add global p-value
  geom_text(data = p_meds, aes(x = multNum, y = med, label = med), 
              size = 3, vjust = -1.5) +
  ggtitle("RT Distribution vs. Number of Multipliers: Absolute Summed Value < $0.50")

p0
```


## RT by Mult for "Easy" trials

```{r}
# create medians to insert as text
d<- S_M
d <- d[d$absSummedVal>1.0, ] # limit to absolute summed values below 0.50
x <- d
x$multNum = d$multNum+1
p_meds <- ddply(x, .(multNum), summarise, med = median(rt))
p_meds$med = round(p_meds$med, digits = 2)  # round to two decimal values

# List of conditions to compare
my_comparisons <- list( c("0", "1"), c("0", "2"), c("1", "2") )

p0 = ggboxplot(d, x = "multNum", y = "rt", color = "multNum", palette = "jco" )+ #outlier.shape=NA
  
  labs(y = "Total RT (seconds)", x = "Number of Multipliers") +
  theme(legend.position="none") +  
  scale_x_discrete(labels=c("0" = "None", "1" = "One",
                              "2" = "Two")) +
  #scale_y_continuous(limits = quantile(d$fixDur, c(0.1, 0.9))) +
  stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
  stat_compare_means(label.y = 13) +     # Add global p-value
  geom_text(data = p_meds, aes(x = multNum, y = med, label = med), 
              size = 3, vjust = -1.5) +
  ggtitle("RT Distribution vs. Number of Multipliers: Absolute Summed Value > $1.00")

p0

d<- S_M
d <- d[d$absSummedVal<0.25, ] # limit to absolute summed values below 0.50
subject_means <- group_by(d, multNum) %>%
  dplyr::summarize(median = mean(correct, na.rm = T))
subject_means

```

## What is the mean abs value of combos with 0/1/2 mults?
### Highest for 1 multiplier

```{r}
d <- S_M

d0 <- d[d$multNum == 0, ]
d1 <- d[d$multNum == 1, ]
d2 <- d[d$multNum == 2, ]

mean(d0$absSummedVal)
mean(d1$absSummedVal)
mean(d2$absSummedVal)
```
# PSYCHOMETRICS
## Study 1/Study 2, RT and Accuracy

```{r}
load("Data/NS_M.Rdata")
d1 <- NS_M
d2 <- S_M

# Create ID for each DF
d1$study <- "Standard Mult."
d2$study <- "Swap Mult."

# Need to uniquely number Subjects
d2$subject <- d2$subject + 100

# Concat DFs
common_cols <- intersect(colnames(d1), colnames(d2))
df = rbind(
  d1[, common_cols], 
  d2[, common_cols]
)

# GROUPBY
subject_means <- group_by(df, subject, study) %>%
  dplyr::summarize(accuracy = mean(correct, na.rm = T), rt = mean(rt, na.rm = T), meanSwap = mean(swapAmount, na.rm = T))
subject_means

# Mean and SD for both studies
# Study 1: Accuracy
mean(subject_means$accuracy[subject_means$study == "Standard Mult."])
sd(subject_means$accuracy[subject_means$study == "Standard Mult."])

# Study 2: Accuracy
mean(subject_means$accuracy[subject_means$study == "Swap Mult."])
sd(subject_means$accuracy[subject_means$study == "Swap Mult."])

# Significance
t.test(subject_means$accuracy[subject_means$study == "Standard Mult."], subject_means$accuracy[subject_means$study == "Swap Mult."])

# Study 1: RT
mean(subject_means$rt[subject_means$study == "Standard Mult."])
sd(subject_means$rt[subject_means$study == "Standard Mult."])

# Study 2: RT
mean(subject_means$rt[subject_means$study == "Swap Mult."])
sd(subject_means$rt[subject_means$study == "Swap Mult."])

t.test(subject_means$rt[subject_means$study == "Standard Mult."], subject_means$rt[subject_means$study == "Swap Mult."])

# Study 2: Swaps
subject_means <- group_by(d2, subject, study) %>%
  dplyr::summarize(accuracy = mean(correct, na.rm = T), rt = mean(rt, na.rm = T), meanSwap = mean(swapAmount, na.rm = T))
subject_means

mean(subject_means$meanSwap)
sd(subject_means$meanSwap)

```


## Create Binned Values to test against Accuracy and RT

```{r}
# Figure out histogram bin size, based on equal numbers of observations
library(Hmisc) # cut2

d <- S_M
d$choice[d$choice == -1] = 0 # -1 for Tavares needs to be 0 in order to calculate prob.

# How many bins?
numBins = 19 # same as Krajbich

# SUMMED VAL
d$valBin <- as.numeric(cut2(d$summedVal, g=numBins))
d$valBinAmt <- cut2(d$summedVal, g=numBins)
d$valBinCtr <- cut2(d$summedVal, g=numBins, levels.mean=TRUE)
vals = as.numeric(as.character(unique(d$valBinCtr)))
vals = sort(vals)

# FOR RT
subject_means_rt <- group_by(d, subject, valBinCtr) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T))
subject_means_rt

# FOR ACCURACY
subject_means_acc <- group_by(d, subject, valBinCtr) %>%
  dplyr::summarize(correct = mean(correct, na.rm = T))
subject_means_acc

# FOR CHOICE
subject_means_choice <- group_by(d, subject, valBinCtr) %>%
  dplyr::summarize(accept = mean(choice, na.rm = T))
subject_means_choice

# Create DF with all bins as columns
# FOR RT
subject_means_wide_rt <-
  spread(subject_means_rt,
         key = valBinCtr,
         value = rt,
         sep = "_")

# FOR ACCURACY
subject_means_wide_acc <-
  spread(subject_means_acc,
         key = valBinCtr,
         value = correct,
         sep = "_")

# FOR CHOICE
subject_means_wide_choice <-
  spread(subject_means_choice,
         key = valBinCtr,
         value = accept,
         sep = "_")

# DF with mean and SD for each bin

rt_x = sapply(subject_means_wide_rt, function(cl) list(means=mean(cl,na.rm=TRUE), sds=sd(cl,na.rm=TRUE)))
rt_x = t(rt_x)
acc_x = sapply(subject_means_wide_acc, function(cl) list(means=mean(cl,na.rm=TRUE), sds=sd(cl,na.rm=TRUE)))
acc_x = t(acc_x)
choice_x = sapply(subject_means_wide_choice, function(cl) list(means=mean(cl,na.rm=TRUE), sds=sd(cl,na.rm=TRUE)))
choice_x = t(choice_x)

# MEANs
rt_mean = numeric()
acc_mean = numeric()
choice_mean = numeric()
for(i in 2:20){
  rt_mean = c(rt_mean, rt_x[i,1][[1]])
  acc_mean = c(acc_mean, acc_x[i,1][[1]])
  choice_mean = c(choice_mean, choice_x[i,1][[1]])
}

# SDs
rt_sd = numeric()
acc_sd = numeric()
choice_sd = numeric()
for(i in 2:20){
  rt_sd = c(rt_sd, rt_x[i,2][[1]])
  acc_sd = c(acc_sd, acc_x[i,2][[1]])
  choice_sd = c(choice_sd, choice_x[i,2][[1]])
}

# Create DF
df = data.frame("val" = vals,
                "rt_mean" = rt_mean, "rt_sd" = rt_sd,
                "acc_mean" = acc_mean, "acc_sd" = acc_sd,
                "choice_mean" = choice_mean, "choice_sd" = choice_sd)

# Add SEs
nVal = sqrt(length(unique(d$subject))) # calculate the denominator of the SE equation
df$rt_se <- df$rt_sd/nVal
df$acc_se <- df$acc_sd/nVal
df$choice_se <- df$choice_sd/nVal

#-----------#
# PLOT      # 
#-----------#

# RT
ggplot(data = df,aes(x = val,y = rt_mean)) + 
  geom_point() + 
  #geom_line() +
  geom_errorbar(aes(ymin = rt_mean-rt_se,ymax = rt_mean+rt_se)) + 
  labs(x = "Net Value", y = "Reaction Time (seconds)") +
  theme_minimal() +
  ggtitle("Mean Reaction Time by Net Value")
  
# ACCURACY
ggplot(data = df,aes(x = val,y = acc_mean)) + 
  geom_point() + 
  #geom_line() +
  geom_errorbar(aes(ymin = acc_mean-acc_se, ymax = acc_mean+acc_se)) + 
  labs(x = "Net Value", y = "p(Correct)") +
  theme_minimal() +
  ggtitle("B") +
  theme(plot.title = element_text(size=22))
#  ggtitle("p(Correct) by Net Value ")

# CHOICE
ggplot(data = df,aes(x = val,y = choice_mean)) + 
  geom_point() + 
  #geom_line() +
  geom_errorbar(aes(ymin = choice_mean-choice_se, ymax = choice_mean+choice_se)) + 
  labs(x = "Net Value", y = "p(Accept)") +
  scale_x_continuous(breaks = seq(-3,3,0.5)) +
  theme_minimal() +
  theme(axis.title.x=element_text(size=17),
        axis.title.y = element_text(size = 17))
  #ggtitle("A") +
  #theme(plot.title = element_text(size=22))

setwd("/Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots")
ggsave("Prob.png", width = 19, height = 12, units = "cm")

#T-TEST for trials
#t.test(subject_means_wide$flip_1, subject_means_wide$flip_2, paired = TRUE)
```

## Same as above but for abs Val for RT and Fixation

```{r}
# ABS Val
d <- S_M
d$choice[d$choice == -1] = 0 # -1 for Tavares needs to be 0 in order to calculate prob.

d$absValBin <- as.numeric(cut2(d$absSummedVal, g=9))
d$absValBinAmt <- cut2(d$absSummedVal, g=9)
d$absValBinCtr <- cut2(d$absSummedVal, g=9, levels.mean=TRUE)
absVals = as.numeric(as.character(unique(d$absValBinCtr)))
absVals = sort(absVals)

# Abs Val RT
abs_subject_means_rt <- group_by(d, subject, absValBinCtr) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T))
abs_subject_means_rt

# FOR Fixations
abs_subject_means_fix <- group_by(d, subject, absValBinCtr) %>%
  dplyr::summarize(fixations = mean(swapAmount, na.rm = T))
abs_subject_means_fix

# Create DF with all bins as columns
# FOR RT
abs_subject_means_wide_rt <-
  spread(abs_subject_means_rt,
         key = absValBinCtr,
         value = rt,
         sep = "_")

# FOR ACCURACY
abs_subject_means_wide_fix <-
  spread(abs_subject_means_fix,
         key = absValBinCtr,
         value = fixations,
         sep = "_")

# DF with mean and SD for each bin

rt_x = sapply(abs_subject_means_wide_rt, function(cl) list(means=mean(cl,na.rm=TRUE), sds=sd(cl,na.rm=TRUE)))
rt_x = t(rt_x)
fix_x = sapply(abs_subject_means_wide_fix, function(cl) list(means=mean(cl,na.rm=TRUE), sds=sd(cl,na.rm=TRUE)))
fix_x = t(fix_x)

rt_x

rt_mean = numeric()
fix_mean = numeric()
for(i in 2:10){
  rt_mean = c(rt_mean, rt_x[i,1][[1]])
  fix_mean = c(fix_mean, fix_x[i,1][[1]])
}

rt_sd = numeric()
fix_sd = numeric()
for(i in 2:10){
  rt_sd = c(rt_sd, rt_x[i,2][[1]])
  fix_sd = c(fix_sd, fix_x[i,2][[1]])
}


df = data.frame("abs_val" = absVals, "rt_mean" = rt_mean, "rt_sd" = rt_sd, "fix_mean" = fix_mean, "fix_sd" = fix_sd)
nVal = sqrt(length(unique(d$subject))) # calculate the denominator of the SE equation
df$rt_se <- df$rt_sd/nVal
df$fix_se <- df$fix_sd/nVal
df

#-----------#
# PLOT      # 
#-----------#

# RT
ggplot(data = df,aes(x = abs_val,y = rt_mean)) + 
  geom_point() + 
  #geom_line() +
  geom_errorbar(aes(ymin = rt_mean-rt_se,ymax = rt_mean+rt_se)) + 
  labs(x = "Absolute Net Value ($)", y = "Reaction Time (s)") +
  scale_x_continuous(breaks = seq(0,3,0.5)) +
  scale_y_continuous(breaks = seq(2.2,3.8,0.2)) +
  theme_minimal()+
  theme(axis.title.x=element_text(size=18),
        axis.title.y = element_text(size = 18))
  ggtitle("Mean Reaction Time by Absolute Net Value")
  
# FIXATIONS
ggplot(data = df,aes(x = abs_val,y = fix_mean)) + 
  geom_point() + 
  #geom_line() +
  geom_errorbar(aes(ymin = fix_mean-fix_se, ymax = fix_mean+fix_se)) + 
  labs(x = "Absolute Net Value ($)", y = "Fixation Count") +
  scale_x_continuous(breaks = seq(0,3,0.5)) +
  scale_y_continuous(breaks = seq(2.4,3.5,0.1)) +
  theme_minimal()+
  theme(axis.title.x=element_text(size=17),
        axis.title.y = element_text(size = 17))
  ggtitle("Fixations by Absolute Net Value ")
  
setwd("/Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots")
ggsave("RT.png", width = 19, height = 12, units = "cm")
  

```


# FUNCTIONS for SUMMARY STATS
### From: http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_(ggplot2)/#Helper%20functions

```{r}
## Norms the data within specified groups in a data frame; it normalizes each
## subject (identified by idvar) so that they have the same mean, within each group
## specified by betweenvars.
##   data: a data frame.
##   idvar: the name of a column that identifies each subject (or matched subjects)
##   measurevar: the name of a column that contains the variable to be summariezed
##   betweenvars: a vector containing names of columns that are between-subjects variables
##   na.rm: a boolean that indicates whether to ignore NA's
normDataWithin <- function(data=NULL, idvar, measurevar, betweenvars=NULL,
                           na.rm=FALSE, .drop=TRUE) {
    library(plyr)

    # Measure var on left, idvar + between vars on right of formula.
    data.subjMean <- ddply(data, c(idvar, betweenvars), .drop=.drop,
     .fun = function(xx, col, na.rm) {
        c(subjMean = mean(xx[,col], na.rm=na.rm))
      },
      measurevar,
      na.rm
    )

    # Put the subject means with original data
    data <- merge(data, data.subjMean)

    # Get the normalized data in a new column
    measureNormedVar <- paste(measurevar, "_norm", sep="")
    data[,measureNormedVar] <- data[,measurevar] - data[,"subjMean"] +
                               mean(data[,measurevar], na.rm=na.rm)

    # Remove this subject mean column
    data$subjMean <- NULL

    return(data)
}

## Summarizes data, handling within-subjects variables by removing inter-subject variability.
## It will still work if there are no within-S variables.
## Gives count, un-normed mean, normed mean (with same between-group mean),
##   standard deviation, standard error of the mean, and confidence interval.
## If there are within-subject variables, calculate adjusted values using method from Morey (2008).
##   data: a data frame.
##   measurevar: the name of a column that contains the variable to be summariezed
##   betweenvars: a vector containing names of columns that are between-subjects variables
##   withinvars: a vector containing names of columns that are within-subjects variables
##   idvar: the name of a column that identifies each subject (or matched subjects)
##   na.rm: a boolean that indicates whether to ignore NA's
##   conf.interval: the percent range of the confidence interval (default is 95%)

summarySEwithin <- function(data=NULL, measurevar, betweenvars=NULL, withinvars=NULL,
                            idvar=NULL, na.rm=FALSE, conf.interval=.95, .drop=TRUE) {

  # Ensure that the betweenvars and withinvars are factors
  factorvars <- vapply(data[, c(betweenvars, withinvars), drop=FALSE],
    FUN=is.factor, FUN.VALUE=logical(1))

  if (!all(factorvars)) {
    nonfactorvars <- names(factorvars)[!factorvars]
    message("Automatically converting the following non-factors to factors: ",
            paste(nonfactorvars, collapse = ", "))
    data[nonfactorvars] <- lapply(data[nonfactorvars], factor)
  }

  # Get the means from the un-normed data
  datac <- summarySE(data, measurevar, groupvars=c(betweenvars, withinvars),
                     na.rm=na.rm, conf.interval=conf.interval, .drop=.drop)

  # Drop all the unused columns (these will be calculated with normed data)
  datac$sd <- NULL
  datac$se <- NULL
  datac$ci <- NULL

  # Norm each subject's data
  ndata <- normDataWithin(data, idvar, measurevar, betweenvars, na.rm, .drop=.drop)

  # This is the name of the new column
  measurevar_n <- paste(measurevar, "_norm", sep="")

  # Collapse the normed data - now we can treat between and within vars the same
  ndatac <- summarySE(ndata, measurevar_n, groupvars=c(betweenvars, withinvars),
                      na.rm=na.rm, conf.interval=conf.interval, .drop=.drop)

  # Apply correction from Morey (2008) to the standard error and confidence interval
  #  Get the product of the number of conditions of within-S variables
  nWithinGroups    <- prod(vapply(ndatac[,withinvars, drop=FALSE], FUN=nlevels,
                           FUN.VALUE=numeric(1)))
  correctionFactor <- sqrt( nWithinGroups / (nWithinGroups-1) )

  # Apply the correction factor
  ndatac$sd <- ndatac$sd * correctionFactor
  ndatac$se <- ndatac$se * correctionFactor
  ndatac$ci <- ndatac$ci * correctionFactor

  # Combine the un-normed means with the normed results
  merge(datac, ndatac)
}

## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
##   data: a data frame.
##   measurevar: the name of a column that contains the variable to be summariezed
##   groupvars: a vector containing names of columns that contain grouping variables
##   na.rm: a boolean that indicates whether to ignore NA's
##   conf.interval: the percent range of the confidence interval (default is 95%)
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
                      conf.interval=.95, .drop=TRUE) {
    library(plyr)

    # New version of length which can handle NA's: if na.rm==T, don't count them
    length2 <- function (x, na.rm=FALSE) {
        if (na.rm) sum(!is.na(x))
        else       length(x)
    }

    # This does the summary. For each group's data frame, return a vector with
    # N, mean, and sd
    datac <- ddply(data, groupvars, .drop=.drop,
      .fun = function(xx, col) {
        c(N    = length2(xx[[col]], na.rm=na.rm),
          mean = mean   (xx[[col]], na.rm=na.rm),
          sd   = sd     (xx[[col]], na.rm=na.rm)
        )
      },
      measurevar
    )

    # Rename the "mean" column    
    datac <- rename(datac, c("mean" = measurevar))

    datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean

    # Confidence interval multiplier for standard error
    # Calculate t-statistic for confidence interval: 
    # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
    ciMult <- qt(conf.interval/2 + .5, datac$N-1)
    datac$ci <- datac$se * ciMult

    return(datac)
}
```


## Difficult, Very Difficult, Easy, Overall RT and Accuracy by MultNum
```{r}
d <- S_M
# Remove abs summed values >1.00 and <0.50
d <- d[(d$absSummedVal<=0.50) | (d$absSummedVal>=1.00),]

# Create Difficulty Level and Factor it
d$difficulty = 1  # easy level
d$difficulty[d$absSummedVal<0.5] = 2 # Difficult level
d$difficulty[d$absSummedVal<0.25] = 3 # V.Difficult level

# Factor conditions
d$multNum <- factor(d$multNum)
d$difficulty <- factor(d$difficulty)

# FOR T-TESTS
subject_means <- group_by(d, subject, difficulty, multNum) %>%
  dplyr::summarize(accuracy = mean(correct), rt = mean(rt))
subject_means

# Paired TTest
# Accuracy
# Easy vs Hard
mean(subject_means$accuracy[subject_means$difficulty == 1])
mean(subject_means$accuracy[subject_means$difficulty == 3])
sd(subject_means$accuracy[subject_means$difficulty == 1])
sd(subject_means$accuracy[subject_means$difficulty == 3])
       
t.test(subject_means$accuracy[subject_means$difficulty == 1],
       subject_means$accuracy[subject_means$difficulty == 3], paired = TRUE)

# Easy, 0 Mult/1 Mult
mean(subject_means$accuracy[subject_means$difficulty == 1 & subject_means$multNum == 0])
mean(subject_means$accuracy[subject_means$difficulty == 1 & subject_means$multNum == 1])
sd(subject_means$accuracy[subject_means$difficulty == 1 & subject_means$multNum == 0])
sd(subject_means$accuracy[subject_means$difficulty == 1 & subject_means$multNum == 1])

t.test(subject_means$accuracy[subject_means$difficulty == 1 & subject_means$multNum == 0],
       subject_means$accuracy[subject_means$difficulty == 1 & subject_means$multNum == 1], paired = TRUE)

# Hard, 0 Mult/1 Mult
mean(subject_means$accuracy[subject_means$difficulty == 3 & subject_means$multNum == 0])
mean(subject_means$accuracy[subject_means$difficulty == 3 & subject_means$multNum == 1])
sd(subject_means$accuracy[subject_means$difficulty == 3 & subject_means$multNum == 0])
sd(subject_means$accuracy[subject_means$difficulty == 3 & subject_means$multNum == 1])

t.test(subject_means$accuracy[subject_means$difficulty == 3 & subject_means$multNum == 0],
       subject_means$accuracy[subject_means$difficulty == 3 & subject_means$multNum == 1], paired = TRUE)

# Paired TTest
# RT
# Easy vs Hard
mean(subject_means$rt[subject_means$difficulty == 1])
mean(subject_means$rt[subject_means$difficulty == 3])
sd(subject_means$rt[subject_means$difficulty == 1])
sd(subject_means$rt[subject_means$difficulty == 3])
       
t.test(subject_means$rt[subject_means$difficulty == 1],
       subject_means$rt[subject_means$difficulty == 3], paired = TRUE)

# Easy, 0 Mult/1 Mult
mean(subject_means$rt[subject_means$difficulty == 1 & subject_means$multNum == 0])
mean(subject_means$rt[subject_means$difficulty == 1 & subject_means$multNum == 1])
sd(subject_means$rt[subject_means$difficulty == 1 & subject_means$multNum == 0])
sd(subject_means$rt[subject_means$difficulty == 1 & subject_means$multNum == 1])

t.test(subject_means$rt[subject_means$difficulty == 1 & subject_means$multNum == 0],
       subject_means$rt[subject_means$difficulty == 1 & subject_means$multNum == 1], paired = TRUE)

# Hard, 0 Mult/1 Mult
mean(subject_means$rt[subject_means$difficulty == 3 & subject_means$multNum == 0])
mean(subject_means$rt[subject_means$difficulty == 3 & subject_means$multNum == 1])
sd(subject_means$rt[subject_means$difficulty == 3 & subject_means$multNum == 0])
sd(subject_means$rt[subject_means$difficulty == 3 & subject_means$multNum == 1])

t.test(subject_means$rt[subject_means$difficulty == 3 & subject_means$multNum == 0],
       subject_means$rt[subject_means$difficulty == 3 & subject_means$multNum == 1], paired = TRUE)

#------------#
# PLOT       #
#------------#

# For RT
# Stats Summary
datac <- summarySEwithin(d, measurevar="rt", withinvars=c("multNum","difficulty"), idvar="subject")

ggplot(datac, aes(x=difficulty, y=rt, fill=multNum)) +
    geom_bar(position=position_dodge(.9), colour="black", stat="identity") +
    geom_errorbar(position=position_dodge(.9), width=.25, aes(ymin=rt-ci, ymax=rt+ci)) +
    coord_cartesian(ylim=c(2,4)) +
    labs(y = "Total RT (s)", x = "Difficulty (net value)") +
    scale_x_discrete(labels=c("1" = "Easy (>1)", "2" = "Difficult (0.5<0.25)",
                              "3" = "Very Difficult (<0.25)")) +
    scale_y_continuous(breaks=seq(2,4,0.2)) +
    theme_bw() +
    scale_fill_discrete(name="Number of\nMultipliers") 
    #ggtitle("RT vs. Difficulty + Number of Multipliers")  

# For Accuracy
# Stats Summary
datac <- summarySEwithin(d, measurevar="correct", withinvars=c("multNum","difficulty"), idvar="subject")

ggplot(datac, aes(x=difficulty, y=correct, fill=multNum)) +
    geom_bar(position=position_dodge(.9), colour="black", stat="identity") +
    geom_errorbar(position=position_dodge(.9), width=.25, aes(ymin=correct-ci, ymax=correct+ci)) +
    coord_cartesian(ylim=c(0.5,1)) +
    labs(y = "Accuracy", x = "Difficulty (net value)") +
    scale_x_discrete(labels=c("1" = "Easy (>1)", "2" = "Difficult (0.5<0.25)",
                              "3" = "Very Difficult (<0.25)")) +    
    scale_y_continuous(breaks=seq(0,1,0.1)) +
    theme_bw() +
    scale_fill_discrete(name="Number of\nMultipliers") 
    #ggtitle("Accuracy vs. Difficulty + Number of Multipliers")  
```


# ANOVA
##ANOVA on difference between *multiplier* and *non multiplier* trials RT

```{r anova-mult, echo=FALSE}
#add multNum column
#total_M_clean3$multNum[total_M_clean3$mult1House==1 & total_M_clean3$mult2Face==1] <- 0
#total_M_clean3$multNum[total_M_clean3$mult1House>1 & total_M_clean3$mult2Face==1] <- 1
#total_M_clean3$multNum[total_M_clean3$mult1House==1 & total_M_clean3$mult2Face>1] <- 1
#total_M_clean3$multNum[total_M_clean3$mult1House>1 & total_M_clean3$mult2Face>1] <- 2


d <- total_M_clean3

subject_means <- group_by(d, subject, multNum) %>%
  dplyr::summarize(rt = mean(RT, na.rm = T))

subject_means$subject <- factor(subject_means$subject)
subject_means$multNum <- factor(subject_means$multNum)

# DV = rt, facto
require(nlme)
am2 <- lme(rt ~ multNum, random = ~1|subject/multNum, data=subject_means)
summary(am2)
```

Based on this, there is no significant effect (as expressed through RT) in having one multiplier, however there *is* for having two.

##

```{r anova-mult3, echo=FALSE}

d <- total_M_clean3

d$subject <- factor(d$subject)
d$multNum <- factor(d$multNum)

#convert d to DataFrame
df <- data.frame(subject=d$subject, multNum=d$multNum, absVal=d$absVal, rt=d$RT)

#THIS IS GIVING AN ERROR, CHECK WITH LIZ!!
#Cendri site: http://rpsychologist.com/r-guide-longitudinal-lme-lmer
#am3 <- lme(rt ~ multNum+absVal, random = , data=df)
#summary(am3)
```

Based on this, there is no significant effect (as expressed through RT) in having one multiplier, however there *is* for having two.

## FIXATION DURATION

```{r}
load("Data/S_M_K.Rdata")
d <- S_M_K

# Factor conditions
d$subject <- factor(d$subject)

# FOR T-TESTS
subject_means <- group_by(d, subject) %>%
  dplyr::summarize(firstFix = mean(fixDur[fixNum == 1]),
                   middleFix = mean(fixDur[fixNum > 1 & revFixNum > 1]),
                   finalFix = mean(fixDur[revFixNum == 1]))
subject_means

# Paired TTest
# RT
mean(subject_means$firstFix)
mean(subject_means$middleFix)
mean(subject_means$finalFix)
sd(subject_means$firstFix)
sd(subject_means$middleFix)
sd(subject_means$finalFix)

t.test(subject_means$middleFix,
       subject_means$finalFix, paired = TRUE)
```

##Linear Models for Dependent (Fixed) Effects (not taking random effects into account)

```{r lms-1, echo=FALSE}
#fit a model using house value and face value as X-variables
model1 <- lm(RT ~ faceVal + houseVal, data = total_M_clean3)
summary(model1)

model2 <- lm(RT ~ faceVal*houseVal, data = total_M_clean3)
summary(model2)

model3 <- lm(RT ~ total_0_face * total_1_house, data = total_M_clean3)
summary(model3)

model4 <- lm(RT ~ mult1House*mult2Face, data = total_M_clean3)
summary(model4)

model5 <- lm(RT ~ (faceVal*mult2Face) * (houseVal*mult1House), data = total_M_clean3)
summary(model5)

#DELETE create face total and house total columns
#DELETE total_M_clean3$faceTotal <- total_M_clean3$faceVal*total_M_clean3$mult2Face
#DELETE total_M_clean3$houseTotal <- total_M_clean3$houseVal*total_M_clean3$mult1House

model6 <- lm(RT ~ faceTotal * houseTotal, data = total_M_clean3)
summary(model6)
```

##First Fixation Duration vs. First Image Total Value
```{r plot-firstFix, echo=FALSE}
#create firstFix column
for(x in 1:nrow(total_M_clean3)){
  total_M_clean3$firstFix[x] <- total_M_clean3$fixation_timing[x][[1]][1]
}
#create firstImage column
for(x in 1:nrow(total_M_clean3)){
  total_M_clean3$firstImage[x] <- total_M_clean3$imageSequence[x][[1]][1]
}
#create firstVal column [face is 0, house is 1]
for(x in 1:nrow(total_M_clean3)){
  if (total_M_clean3$firstImage[x] == 0){
    total_M_clean3$firstVal[x] <- total_M_clean3$faceTotal[x]
  }
  if (total_M_clean3$firstImage[x] == 1){
    total_M_clean3$firstVal[x] <- total_M_clean3$houseTotal[x]
  }
}

total_M_clean3$test <- NULL


#And for later lets also create secondFix and secondVal
#secondFix column
for(x in 1:nrow(total_M_clean3)){
  total_M_clean3$secondFix[x] <- total_M_clean3$fixation_timing[x][[1]][2]
}
#create secondVal column [face is 0, house is 1 BUT since it is the second image it is the opposite]
for(x in 1:nrow(total_M_clean3)){
  if (total_M_clean3$firstImage[x] == 1){
    total_M_clean3$secondVal[x] <- total_M_clean3$faceTotal[x]
  }
  if (total_M_clean3$firstImage[x] == 0){
    total_M_clean3$secondVal[x] <- total_M_clean3$houseTotal[x]
  }
}

#plot
ggplot(total_M_clean3, aes(x=firstVal, y=firstFix)) +
  geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  # Add a loess smoothed fit curve with confidence region

#Make 1st and 2nd Vals Absolute
total_M_clean3$absFirstVal = abs(total_M_clean3$firstVal)
total_M_clean3$absSecondVal = abs(total_M_clean3$secondVal)

#First Fix, First Val
ggplot(total_M_clean3, aes(x=absFirstVal, y=firstFix)) +
  geom_point(shape=1) +    # Use hollow circles
  geom_smooth()

summary(lm(firstFix~absFirstVal, data=total_M_clean3))

#Second Fix, Second Val
ggplot(total_M_clean3, aes(x=absSecondVal, y=secondFix)) +
  geom_point(shape=1) +    # Use hollow circles
  geom_smooth()

summary(lm(secondFix~absSecondVal, data=total_M_clean3))

#Does the first value affect the second fixation?
summary(lm(secondFix~absFirstVal, data=total_M_clean3))

#Do the first and second values together affect the second fixation?
summary(lm(secondFix~absFirstVal + absSecondVal, data=total_M_clean3))

#Is there an interaction between first and second val on the second fixation?
summary(lm(secondFix~absFirstVal*absSecondVal, data=total_M_clean3))

```

##First Fixation Duration vs. First Mult
```{r plot-firstFix-Mult, echo=FALSE}

#create firstMult column [face is 0, house is 1]
for(x in 1:nrow(total_M_clean3)){
  if (total_M_clean3$firstImage[x] == 0){
    total_M_clean3$firstMult[x] <- total_M_clean3$mult2Face[x]
  }
  if (total_M_clean3$firstImage[x] == 1){
    total_M_clean3$firstMult[x] <- total_M_clean3$mult1House[x]
  }
}

#create secondMult column (reverse the house/face values)
for(x in 1:nrow(total_M_clean3)){
  if (total_M_clean3$firstImage[x] == 1){
    total_M_clean3$secondMult[x] <- total_M_clean3$mult2Face[x]
  }
  if (total_M_clean3$firstImage[x] == 0){
    total_M_clean3$secondMult[x] <- total_M_clean3$mult1House[x]
  }
}

#BAR PLOT
subject_means <- group_by(total_M_clean3, subject, firstMult) %>%
  dplyr::summarize(rt = mean(RT, na.rm = T))

#PLOT
barplot <- ggplot(subject_means, aes(x = firstMult, y = rt)) +
  stat_summary(
    geom = "bar",
    fun.y = "mean",
    col = "black",
    fill = "gray70"
  ) +
  geom_point(position = position_jitter(h = 0, w = 0.2)) +
  scale_y_continuous(limits = c(0, max(d$RT, na.rm = T)),
                     expand = c(0, 0))
barplot

#Size of first mult SIG on RT?
summary(lm(logRT~firstMult, data=total_M_clean3))
```

#APRIL 24: NEW ANALYSES 

##Mixed Models

###FOR STARTERS: Does summed value affect RT (using log RT)?
```{r mix-model-rt-01, echo=FALSE}
rt.null = lmer(logRT ~ 1 + (1|subject), data = total_M_clean3, REML = FALSE)
rt.model1 = lmer(logRT ~ summedVal + (1|subject), data=total_M_clean3, REML=FALSE)

anova(rt.null,rt.model1)
```

Perhaps unsurprisingly based on what we plotted before, summed value has a highly significant effect on reaction time (controlling for random effects of subjects).


###Summed Value as the individual TOTAL values (the value x multiplier) of the FACE and HOUSE
```{r mix-model-rt-02, echo=FALSE}
rt.null = lmer(logRT ~ 1 + (1|subject), data = total_M_clean3, REML = FALSE)
rt.model2 = lmer(logRT ~ faceTotal + houseTotal + (1|subject), data=total_M_clean3, REML=FALSE)

summary(rt.model2)
anova(rt.null,rt.model2)
anova(rt.model1, rt.model2)
```

Based on this there is a significant difference between mean RT and rt.model2 as well as between rt.model2 and rt.model1


###What about interaction between faceTotal and houseTotal?
```{r mix-model-rt-03, echo=FALSE}
rt.model3 = lmer(logRT ~ faceTotal * houseTotal + (1|subject), data=total_M_clean3, REML=FALSE)

summary(rt.model3)
anova(rt.model2, rt.model3)
```

So there is a significant interaction bewteen the total house value and the total face value (as expected).


###And then what if we look at the components (value * multiplier) of the Total Face and Total House Value? 
```{r mix-model-rt-04, echo=FALSE}
rt.model4 = lmer(logRT ~ faceVal * mult2Face * houseVal * mult1House + (1|subject), data=total_M_clean3, REML=FALSE)

summary(rt.model4)
anova(rt.model3, rt.model4)
```
Again, based on the anova analysis there seems to be significance in the interactions between the values and the multipliers.

-----------------------------------------
-----------------------------------------


#RT PLOTS
##Plot RT random effects of subjects
```{r plot-rt-randEffect, echo=FALSE}
#RT vs. Summed Value
library(merTools)       ## for lmer(), sleepstudy
fit <- lmer(logRT ~ summedVal + (summedVal|subject), total_M_clean3)
randoms <- REsim(fit, n.sims = 500)

plotREsim(randoms)
```

##Plot RT with multiple lines (average of each subjects line)
* multiplier trials
* flip trials
* non flip trials
* non mult trials

```{r plot-rt-multlines01, echo=FALSE}
#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=summedVal, y=logRT, group = factor(multNum), colour = factor(multNum)), total_M_clean3) +
  geom_smooth(aes(x=summedVal, y=logRT, colour = "flip"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  # Add a loess smoothed fit curve with confidence region

#create multnum as factor
total_M_clean3$multNumF = factor(total_M_clean3$multNum)
#Test for SIG
summary(lm(logRT~summedVal + multNumF + flip, total_M_clean3))
```

Note that with the flip trials the summed values ranged from -0.6 to 0.6.

##Plot Accuracy (% correct) with multiple lines (average of each subjects line)
* multiplier trials
* flip trials
* non flip trials
* non mult trials

```{r plot-rt-multlines02, echo=FALSE}
#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=summedVal, y=correct, group = factor(multNum), colour = factor(multNum)), total_M_clean3) +
  geom_smooth(aes(x=summedVal, y=correct, colour = "flip"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  # Add a loess smoothed fit curve with confidence region
```

###Same Plot but just for "Difficult" choices
```{r plot-rt-multlines09, echo=FALSE}
#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=summedVal, y=correct, group = factor(multNum), colour = factor(multNum)), total_M_clean3) +
  geom_smooth(aes(x=summedVal, y=correct, colour = "flip"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-.5, .5))  +
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  # Add a loess smoothed fit curve with confidence region

mean(total_M_clean3$RT[total_M_clean3$multNum==0])
mean(total_M_clean3$RT[total_M_clean3$multNum==1])
mean(total_M_clean3$RT[total_M_clean3$multNum==2])


d <- total_M_clean3
#1 mult and 2 mult sig. different for SummmedVal interval (-0.5, 0.5)
t.test(d$correct[d$multNum == 0 & d$summedVal>-0.5 & d$summedVal<0.5], d$correct[d$multNum==1 & d$summedVal>-0.5 & d$summedVal<0.5])

```

###RT for difficult choices by multNum
```{r plot-rt-multlines19, echo=FALSE}
#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=summedVal, y=RT, group = factor(multNum), colour = factor(multNum)), total_M_clean3) +
  geom_smooth(aes(x=summedVal, y=RT, colour = "flip"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-1, 1))  +
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  # Add a loess smoothed fit curve with confidence region

d <- total_M_clean3
#1 mult and 2 mult sig. different for SummmedVal interval (-0.5, 0.5) YES
t.test(d$RT[d$multNum == 0 & d$summedVal>-0.5 & d$summedVal<0.5], d$RT[d$multNum==1 & d$summedVal>-0.5 & d$summedVal<0.5])

#1 mult and 2 mult sig. different for SummmedVal interval (-2, -0.5) NO
t.test(d$RT[d$multNum == 0 & d$summedVal< (-0.5) & d$summedVal>-2], d$RT[d$multNum==1 & d$summedVal<(-0.5) & d$summedVal>-2])

#1 mult and 2 mult sig. different for SummmedVal interval (0.5,2) NO
t.test(d$RT[d$multNum == 0 & d$summedVal>0.5 & d$summedVal<2], d$RT[d$multNum==1 & d$summedVal>0.5 & d$summedVal<2])

```


##CHOICE CURVE
% acceptance (sinusoid)
    -sinusoid for non mult/mult/flip
    
##Value of first item vs. First item fixation time
* no mult
* 2x mult
* 3x mult
```{r plot-firstval-multlines01, echo=FALSE}
#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=firstVal, y=firstFix, group = factor(firstMult), colour = factor(firstMult)), total_M_clean3) +
  #geom_smooth(aes(x=summedVal, y=logRT, colour = "flip"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  # Add a loess smoothed fit curve with confidence region
```

##Value of second item vs. Second item fixation time
* no mult
* 2x mult
* 3x mult

```{r plot-secondVal-multlines01, echo=FALSE}
#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=secondVal, y=secondFix, group = factor(secondMult), colour = factor(secondMult)), total_M_clean3) +
  #geom_smooth(aes(x=summedVal, y=logRT, colour = "flip"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  # Add a loess smoothed fit curve with confidence region
```

##Summed value vs second item fixation time

```{r plot-summedValFix-multlines01, echo=FALSE}
#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=summedVal, y=secondFix, group = factor(multNum), colour = factor(multNum)), total_M_clean3) +
  #geom_smooth(aes(x=summedVal, y=logRT, colour = "flip"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  # Add a loess smoothed fit curve with confidence region
```

##Number of swaps
* based on summed value

```{r plot-swapCount-summedVal2, echo=FALSE}
#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=summedVal, y=swapCount, group = factor(multNum), colour = factor(multNum)), total_M_clean3) +
  #geom_smooth(aes(x=summedVal, y=logRT, colour = "flip"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-3, 3))  +
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  # Add a loess smoothed fit curve with confidence region
```

* based on ambiguity of individual stimuli (ie. closer to zero)
    -stimulus left has ambiguity X, stimulus right has ambiguity Y, summed value has ambiguity Z
    -how much does individual ambiguity vs combined ambiguity affect RT/swaps

```{r plot-swapCount-summedVal, echo=FALSE}
#RT vs. Summed Value
ggplot() +
  geom_smooth(aes(x=faceVal, y=swapCount, colour = "faceVal"), total_M_clean3) +
  geom_smooth(aes(x=houseVal, y=swapCount, colour = "houseVal"), subset(total_M_clean3, flip==1)) +
  coord_cartesian(xlim = c(-1, 1))  +
  ggtitle("Image swaps vs. Ambiguity of Stimulus") +
  labs(x = "Value")
  #geom_point(shape=1) +    # Use hollow circles
  geom_smooth()  # Add a loess smoothed fit curve with confidence region
```

##SALIENCY TEST: Mean Fixation House v. Face
```{r posNeg-barplot111, echo=FALSE}
#T TEST HOUSE v FACE FIX TIME
mean(total_M_clean3$total_1_house)
mean(total_M_clean3$total_0_face)
t.test(total_M_clean3$total_1_house, total_M_clean3$total_0_face)
#PLOT THIS

############
# ABS FACE VALUE GREATER
############

#FIX TIME ON FACE
mean(total_M_clean3$total_0_face[abs(total_M_clean3$faceVal) > abs(total_M_clean3$houseVal)])
#FIX TIME ON HOUSE
mean(total_M_clean3$total_1_house[abs(total_M_clean3$faceVal) > abs(total_M_clean3$houseVal)])
#TTEST
t.test((total_M_clean3$total_0_face[abs(total_M_clean3$faceVal) > abs(total_M_clean3$houseVal)]), (total_M_clean3$total_1_house[abs(total_M_clean3$faceVal) > abs(total_M_clean3$houseVal)]))

############
# ABS HOUSE VALUE GREATER
############

#FIX TIME ON HOUSE
mean(total_M_clean3$total_1_house[abs(total_M_clean3$faceVal) < abs(total_M_clean3$houseVal)])
#FIX TIME ON FACE
mean(total_M_clean3$total_0_face[abs(total_M_clean3$faceVal) < abs(total_M_clean3$houseVal)])
#TTEST
t.test((total_M_clean3$total_1_house[abs(total_M_clean3$faceVal) < abs(total_M_clean3$houseVal)]), (total_M_clean3$total_0_face[abs(total_M_clean3$faceVal) < abs(total_M_clean3$houseVal)]))

## We look longer at the item with lower absolute value!!
  ## What about case where one is positive the other negative

############
# THE DECIDER FIXATION
############
# Do we look longer in trials with opposite signed stimuli at the item with higher abs val?

#Make Factor Column for FACE/HOUSE (0/1) pp, nn, pn, np
total_M_clean3$posNeg <- "pp"
total_M_clean3$posNeg[total_M_clean3$faceVal>0 & total_M_clean3$houseVal<0] <- "pn"
total_M_clean3$posNeg[total_M_clean3$faceVal<0 & total_M_clean3$houseVal>0] <- "np"
total_M_clean3$posNeg[total_M_clean3$faceVal<0 & total_M_clean3$houseVal<0] <- "nn"

total_M_clean3$posNeg <- factor(total_M_clean3$posNeg)

#Make Column for Fixation Bias
total_M_clean3$fixBias <- total_M_clean3$total_0_face - total_M_clean3$total_1_house

d <- total_M_clean3

ggplot(d) +
  geom_bar(aes(posNeg, fixBias),
           position = "dodge", stat = "summary", fun.y = "mean")
```
People looked at HOUSES longer. More salient? Or more ambiguous?

##FINAL FIXATION: Tied to Value?
```{r posNeg-barplot19, echo=FALSE}

#ADD Column for total FACE and HOUSE VALS
total_M_clean3$totalFaceVal <- total_M_clean3$faceVal * total_M_clean3$mult2Face
total_M_clean3$totalHouseVal <- total_M_clean3$houseVal * total_M_clean3$mult1House

library(nlme)
d <- total_M_clean3

#######
# NO MULT, JUST VALUE
#######

#LAST IMAGE = FACE (0)
mean(d$faceVal[d$lastImage==0])
mean(d$houseVal[d$lastImage==0])
## Faces +ve, Houses -ve

#LAST IMAGE = HOUSE (1)
mean(d$faceVal[d$lastImage==1])
mean(d$houseVal[d$lastImage==1])
## Houses +ve, Faces -ve

#######
# MULT * VALUE
#######

#LAST IMAGE = FACE (0)
mean(d$totalFaceVal[d$lastImage==0])
mean(d$totalHouseVal[d$lastImage==0])
## Faces +ve, Houses -ve

#LAST IMAGE = HOUSE (1)
mean(d$totalFaceVal[d$lastImage==1])
mean(d$totalHouseVal[d$lastImage==1])
## Houses +ve, Faces -ve


#Make Last image Factor
d$lastImageF <- factor(d$lastImage)
#Sig Effect?
summary(lme(faceVal ~ lastImageF, random = ~1|subject, data=d))

#Tend to look last at image that has higher/positve value

ggplot(d, aes(x=faceVal, y=houseVal)) +
  geom_point(aes(colour=lastImageF))
```



###Are people taking longer for flip trials after accounting for fact that flip trials ALWAYS have multipliers (and non-flip trials don’t)  

This is not currently working

```{r mix-model-01, echo=FALSE}

#lmer(rt ~ multNum + flip + (1|subject) + (multNum|subject) + (flip|subject), data = total_M_clean3)
```

##Questionnaire Data

### Plotting GPA vs. Earnings (unfiltered data):

```{r RT_Earnings-GPA-unfiltered, echo=FALSE}
#Select dataframe to use
d <- S_M_raw

#import Questionnaire data
setwd("~/Dropbox/PHD/CENDRI/Project/Code/LabSharedFolder/MADE01/CODE/GIT/Behavior_Analysis")
Quest.df <- read.csv("csv_files/Questionnaire01_Results.csv")
Quest.df <- Quest.df[(Quest.df$study_version == 2), ]

#mean RT and Final earnings by subject
subject_means <- group_by(d, subject) %>%
  dplyr::summarize(rt = mean(RT, na.rm = T), finalEarnings = mean(finalEarnings, na.rm = T))

subject_info <- group_by(Quest.df, subject) %>%
  dplyr::summarize(gpa = mean(GPA, na.rm = T), effort = mean(Effort, na.rm = T), guess = mean(Guessing, na.rm = T), comparative = mean(Compared_to_others, na.rm = T))

subject_means <- merge(subject_means, subject_info, by = "subject")
subject_means

#BASED ON GPA
subject_means_gpa <- na.omit(subject_means)
plot(x = subject_means_gpa$gpa, y = subject_means_gpa$finalEarnings,
     main = "Performance as related to GPA",
     ylab = "Final Earnings",
     xlab = "GPA")
abline(lm(subject_means_gpa$finalEarnings~subject_means_gpa$gpa), col="red") # regression line (y~x) 
lines(lowess(subject_means_gpa$gpa,subject_means_gpa$finalEarnings), col="blue") # lowess line (x,y)

#TEST FOR SIG.
summary(lm(finalEarnings~gpa, subject_means_gpa))

#BASED ON EFFORT
plot(x = subject_means$effort, y = subject_means$finalEarnings,
     main = "Performance as related to perceived effort",
     ylab = "Final Earnings",
     xlab = "Reported Effort")
abline(lm(subject_means$finalEarnings~subject_means$effort), col="red") # regression line (y~x) 
lines(lowess(subject_means$effort,subject_means$finalEarnings), col="blue") # lowess line (x,y)

#Test for Significance
summary(lm(finalEarnings~effort, subject_means))

#BASED ON GUESSING
plot(x = subject_means$guess, y = subject_means$finalEarnings,
     main = "Performance as related to guess frequency",
     ylab = "Final Earnings",
     xlab = "Reported Guess Frequency")
abline(lm(subject_means$finalEarnings~subject_means$guess), col="red") # regression line (y~x) 
lines(lowess(subject_means$guess,subject_means$finalEarnings), col="blue") # lowess line (x,y)

#TEST FOR SIGNIFICANCE
summary(lm(finalEarnings~guess, subject_means))

#BASED ON COMPARISON
plot(x = subject_means$comparative, y = subject_means$finalEarnings,
     main = "Performance vs. Comparartive Self-Assessment",
     ylab = "Final Earnings",
     xlab = "Reported Comparative Performance")
abline(lm(subject_means$finalEarnings~subject_means$comparative), col="red") # regression line (y~x) 
lines(lowess(subject_means$comparative,subject_means$finalEarnings), col="blue") # lowess line (x,y)

#TEST FOR SIGNIFICANCE compared to others
summary(lm(finalEarnings~comparative, subject_means))
#with(subject_means, cor.test(finalEarnings,comparative))

summary(lm(finalEarnings~effort, subject_means))
summary(lm(finalEarnings~effort+comparative, subject_means))
summary(lm(finalEarnings~effort*comparative*guess, subject_means))
```

##Stroop Data

```{r Stroop_01, echo=FALSE}
#find subject accruacy (uncleaned)
accuracy = tapply(Stroop.df.full$Response.corr==1, Stroop.df.full$subject, mean)

#hists of rt based on congruent and incongruent trials
hist(Stroop.df.clean[Stroop.df.clean$congruent==1, ]$Response.rt,
   col=rgb(1,0,0,0.5), breaks=seq(0,2.5,0.05), ylim=c(0,200), xlab="RT", main = "RT vs Frequency")
hist(Stroop.df.clean[Stroop.df.clean$congruent==0, ]$Response.rt,
   col=rgb(0,0,1,0.5), breaks=seq(0,2.5,0.05), ylim=c(0,200), add=T)
legend("topright", c("Congruent", "Incongruent"), fill=c(rgb(1,0,0,0.5), rgb(0,0,1,0.5)))

#create rts for each subject based on congruent/incongruent
rt_by_condition = tapply(Stroop.df.clean$Response.rt, list(Stroop.df.clean$subject, Stroop.df.clean$congruent), mean)
#convert to data frame
rt_by_condition = as.data.frame(rt_by_condition) 
names(rt_by_condition) = c("incongruent", "congruent")

#get means
mean_rts = apply(rt_by_condition, 2, mean)
#get SE
nsubj = length(rt_by_condition[,1])
sds = apply(rt_by_condition, 2, sd)
se = sds/sqrt(nsubj)

#CREATE A BARPLOT
x=barplot(mean_rts, col=c(rgb(1,0,0,0.5),rgb(0,0,1,0.5)),main="RT
   in each condition",xlab="Condition",ylab="RT",ylim = c(0,1.3))
segments(x, mean_rts-se, x, mean_rts+se)

#Test for SIG
Stroop.df.clean$congruent = as.factor(Stroop.df.clean$congruent)
table(Stroop.df.clean$congruent)
summary(lm(Response.rt~congruent, Stroop.df.clean))
```

###ANOVA for significance

```{r anova-stroop, echo=TRUE}
#reformat Data Frame
found = which(rt_by_condition!=-999,arr.ind=T)
rtANOVA = data.frame(cbind(found,rt_by_condition[found]))
names(rtANOVA) = c('subj','cond','rt')

rtANOVA$subj = factor(rtANOVA$subj)
rtANOVA$cond = factor(rtANOVA$cond)

myaov = aov(rtANOVA$rt~rtANOVA$cond+Error(rtANOVA$subj))
summary(myaov)
```

###T-Test

```{r ttest-stroop, echo=TRUE}
#reformat Data Frame
t.test(rt_by_condition$congruent,rt_by_condition$incongruent,paired=T,mu=0,alternative="two.sided",var.equal=T)

```

Based on Anova/TTest seems like there is a significant difference.

```{r stroop-performance, echo=TRUE}
#Select dataframe to use
d <- Stroop.df.clean

#mean RT and Final earnings by subject
Stroop.performance <- group_by(d, subject) %>%
  dplyr::summarize(rt = mean(Response.rt, na.rm = T), accuracy = mean(Response.corr, na.rm = T))
Stroop.performance$performance = Stroop.performance$rt * 1/Stroop.performance$accuracy
#invert so bigger nunbers are better
Stroop.performance$performance = 1/Stroop.performance$performance
```


##Stroop Performance vs. Task Performance
###vs Earnings

```{r stroop-dotplot, echo=FALSE}
#Select dataframe to use
d <- total_M_clean3

#mean RT and Final earnings by subject
subject_means <- group_by(d, subject) %>%
  dplyr::summarize(rt = mean(RT, na.rm = T), finalEarnings = mean(finalEarnings, na.rm = T))
subject_means

plot(x = Stroop.performance$performance, y = subject_means$finalEarnings,
     main = "Stroop Performance vs Final Earnings",
     ylab = "Final Earnings",
     xlab = "Stroop Performance")
abline(lm(subject_means$finalEarnings~Stroop.performance$performance), col="red") # regression line (y~x) 
lines(lowess(Stroop.performance$performance, subject_means$finalEarnings), col="blue") # lowess line (x,y)
```

###vs Accuracy

```{r stroop-dotplot2, echo=FALSE}
#Select dataframe to use
d <- total_M_clean3

#mean RT and Final earnings by subject
subject_means <- group_by(d, subject) %>%
  dplyr::summarize(rt = mean(RT, na.rm = T), accuracy = mean(correct, na.rm = T))
subject_means

plot(x = Stroop.performance$performance, y = subject_means$accuracy,
     main = "Stroop Performance vs Accuracy",
     ylab = "Accuracy",
     xlab = "Stroop Performance")
abline(lm(subject_means$accuracy~Stroop.performance$performance), col="red") # regression line (y~x) 
lines(lowess(Stroop.performance$performance, subject_means$accuracy), col="blue") # lowess line (x,y)

#Test for SIG
summary(lm(subject_means$accuracy~Stroop.performance$performance))
```

###vs RT

```{r stroop-dotplot3, echo=FALSE}
#Select dataframe to use
d <- total_M_clean3

#mean RT and Final earnings by subject
subject_means <- group_by(d, subject) %>%
  dplyr::summarize(rt = mean(RT, na.rm = T), finalEarnings = mean(finalEarnings, na.rm = T))
subject_means

plot(x = Stroop.performance$performance, y = subject_means$rt,
     main = "Stroop Performance vs RT",
     ylab = "RT",
     xlab = "Stroop Performance")
abline(lm(subject_means$rt~Stroop.performance$performance), col="red") # regression line (y~x) 
lines(lowess(Stroop.performance$performance, subject_means$rt), col="blue") # lowess line (x,y)

#Test for SIG
summary(lm(subject_means$rt~Stroop.performance$performance))
```

##EFFECT SIZE
###Cohen's D

```{r cohens-d-01, echo=TRUE}
#Select dataframe to use
d <- total_M_clean3

#mean RT and Final earnings by subject
rt_mults <- group_by(d, subject) %>%
  dplyr::summarize(mult_0 = mean(RT[multNum==0]), mult_1 = mean(RT[multNum==1], na.rm = T), mult_2 = mean(RT[multNum==2], na.rm=T))
rt_mults

library(lsr)
cohensD(rt_mults$mult_0, rt_mults$mult_1)
cohensD(rt_mults$mult_0, rt_mults$mult_2)
```


##MEANS
###List of Mean RTs for Mults

```{r multmeans-01, echo=TRUE}
#Select dataframe to use
d <- total_M_clean3

#mean RT depending on multiplier combination
for(i in 1:3){
  for(j in 1:3){
    m <- mean(d$RT[d$mult1House==i & d$mult2Face==j])
    cat(sprintf("House Mult = %s and Face Mult = %s\n", i, j))
    cat(sprintf("Mean: %f\n\n", m))
  }
}
```


##MORE MIXED EFFECTS STUFF

###
```{r ranef_01, echo=FALSE}
#FROM: https://biologyforfun.wordpress.com/2017/04/03/interpreting-random-effects-in-linear-mixed-effect-models/

library(reshape2)
m_avg <- lmer(RT ~ 1 + (1|subject), total_M_clean3)
ranef(m_avg)

#to get the fitted average reaction time per subject
reaction_subject <- fixef(m_avg) + ranef(m_avg)$subject
reaction_subject$subject<-rownames(reaction_subject)
names(reaction_subject)[1]<-"Intercept"
reaction_subject <- reaction_subject[,c(2,1)]
#plot
ggplot(reaction_subject,aes(x=subject,y=Intercept))+geom_point()
```

###Simulate RTs based on data
```{r ranef_02, echo=FALSE}
#This line create a dataframe for 18 hypothetical new subjects
#taking the estimated standard deviation reported in
#summary(m_avg) and take SUBJECT SD
new_subject <- data.frame(subject = as.character(50:74),
  Intercept= fixef(m_avg)+rnorm(25,0,1.006),Status="Simulated")
reaction_subject$Status <- "Observed"
reaction_subject <- rbind(reaction_subject,new_subject)
#new plot
ggplot(reaction_subject,aes(x=subject,y=Intercept,color=Status))+
  geom_point()+
  geom_hline(aes(yintercept = fixef(m_avg)[1],linewidth=1.5))
```

##Abs Val vs. RT
###Subject Level

```{r subject-level-graphs, echo=FALSE}
#the next line put all the estimated intercept and slope per subject into a dataframe
#Summed val as Absolute (distance from ambiguity)

#m_slp <- lmer(logRT ~ absVal + (1|subject) + (absVal|subject), total_M_clean3, REML = FALSE)
remove(subject_means)
m_slp <- lmer(logRT ~ absVal + (absVal|subject), total_M_clean3)

#subject differences in intercept and slope into dataframe
df <- data.frame(coef(m_slp)[[1]])

#Just some renaming tidying
df$subject<-rownames(df)
names(df)[1]<-"intercept"
df <- df[,c(3,1,2)]

#bin RTs
d <- total_M_clean3
d$valBin = cut(d$absVal, c(-Inf, 0.5, 1, 1.5, 2, 2.5, 3, Inf), labels = 1:7)

subject_means <- group_by(d, subject, valBin) %>%
  dplyr::summarize(rt = mean(logRT, na.rm = T))

#Hist showing distrib. of RTs (should we log transform?)
hist(d$absVal)

#Convert subject to a factor and valBin to numeric
subject_means$subject = as.factor(subject_means$subject)
subject_means$valBin = as.numeric(subject_means$valBin)
df$subject = as.factor(df$subject)

#Need sequentially ordered subjects
subject_means$subjectOrdered = 0
x=1
for(i in 1:24){
  for(j in 1:7){
    subject_means$subjectOrdered[x] = i
    x=x+1
  }
}

df$subjectOrdered = 0
for(i in 1:24){
  df$subjectOrdered[i] = i
}

#rt_predicted = subject intercept + slope*MIDDLE of Bin Value
#INTERCEPT
subject_means$intercept = 0
for(i in 1:24){
  subject_means$intercept[subject_means$subjectOrdered==i] = df$intercept[df$subjectOrdered==i] 
}
#RT_PREDICT (using slope)
subject_means$rt_predict = 0
for(i in 1:24){
  for(j in 1:7){
    subject_means$rt_predict[subject_means$subjectOrdered==i&subject_means$valBin==j] =
      df$absVal[df$subjectOrdered==i] * (j*0.5-0.25) + subject_means$intercept[i*7] 
    #subtracting 0.25 to get value in middle of bin...as the binned value is the average of all values within the bin range
  }
}

#plot with actual data
ggplot(subject_means,aes(x=valBin,y=rt_predict,color=subject))+
  geom_line()+
  geom_point(data=subject_means,aes(x=(valBin),y=rt))+
  facet_wrap(~subject,nrow=6)
```

## APA Format Plotting
```{r}
apatheme=theme_bw()+
  theme(panel.grid.major=element_blank(),
        panel.grid.minor=element_blank(),
        panel.border=element_blank(),
        axis.line=element_line(),
        text=element_text(family='Times'))
```

##LME
###with help from Liz

```{r lme-liz01, echo=TRUE}
library(lme4)
library(nlme)
library(sjPlot)
d <- S_M
am2 <- lme(logRT ~ multNum+absSummedVal, random = ~1+multNum|subject, data=d)
summary(am2)

#Assuming the former, though (i.e., you meant multNum to be a random slope), then you would additionally add a random slope for absVal:

d$multNumF <- factor(d$multNum)
ctrl <- lmeControl(opt='optim');
am2 <- lme(logRT ~ multNumF+absSummedVal, random = ~1+multNumF+absSummedVal|subject, control=ctrl, data=d)
summary(am2)


###FROM BEFORE##
am2 <- lme(logRT ~ multNumF, random = ~1|subject/multNumF, data=total_M_clean3)
summary(am2)

#------------------------------------------------------#
# NEW MIXED EFFECTS MODELING BASED ON RESULTS SECTION  #
#------------------------------------------------------#

#----------------------#
# 1. STUDY COMPARISON  #
#----------------------#
# Are their perforamnce differences that are caused by the difference in study paradigm?

#----------------------#
# Manipulate Data      #
#----------------------#

# Combine the Swap and Non-Swap Experiments and label them by Study
load("Data/NS_M.Rdata")

d1 <- NS_M
d2 <- S_M

# Create ID for each DF
d1$study <- "Standard Mult."
d2$study <- "Swap Mult."

# Need to uniquely number Subjects
d2$subject <- d2$subject + 100

# Concat DFs
common_cols <- intersect(colnames(d1), colnames(d2))
df = rbind(
  d1[, common_cols], 
  d2[, common_cols]
)

# Study and subjects as factor
df$study <- factor(df$study)
df$subject <- factor(df$subject)

# Create logRT column
df$logRT = log(df$rt)

#----------------------#
# Mixed Effects Models #
#----------------------#

# null model, grouping by school but not fixed effects.
null <-glmer(correct ~ 1 + (1|subject), family = binomial("logit"), data=df)
summary(null)

# Model with fixed effects
fit <- glmer(correct ~ summedVal*study + abs(summedVal)*study + (1 + summedVal + abs(summedVal)|subject), family = binomial("logit"), data = df)
summary(fit)

# null model, grouping by school but not fixed effects.
null <-lmer(logRT ~ 1 + (1|study) + (1|subject), data=df, REML = FALSE)
summary(null)

# Model with fixed effects
fit <- lmer(logRT ~ summedVal*study + abs(summedVal)*study + (1 + summedVal + abs(summedVal)|subject), data = df, REML = FALSE)
summary(fit)
library(car)  # note the critique of using this (http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#what-are-the-p-values-listed-by-summaryglmerfit-etc.-are-they-reliable)
Anova(fit)

# Is choice affected by Net Value?
fit <- glmer(choice ~ summedVal + (1 + summedVal | subject), family = binomial("logit"), data = d)
summary(fit)

# Is accuracy affected by net/abs value?
fit <- glmer(correct ~ summedVal + abs(summedVal) + (1 + summedVal + abs(summedVal) | subject), family = binomial("logit"), data = d)
summary(fit)

# Is choice affected by interaction Net Value x Mult?
fit <- glmer(choice ~ summedVal * factor(multNum) + (1 + summedVal * factor(multNum) | subject), family = binomial("logit"), data = d)
summary(fit)

# Is choice affected by interaction between individual attributes and their individual mults?
fit <- glmer(choice ~ faceVal * mult2Face + houseVal * mult1House + (1 + faceVal * mult2Face + houseVal * mult1House | subject), family = binomial("logit"), data = d)
summary(fit)

# Is accuracy affect by interaction between abs/summed val and mults?
fit <- glmer(correct ~ summedVal * factor(multNum) + abs(summedVal) * factor(multNum) + (1 + summedVal * factor(multNum) + abs(summedVal) * factor(multNum) | subject), family = binomial("logit"), control = glmerControl(optimizer = "bobyqa"), nAGQ = 10, data =d)

fit <- glmer(correct ~ summedVal * factor(multNum) + abs(summedVal) * factor(multNum) + (1 + summedVal * factor(multNum) + abs(summedVal) * factor(multNum) | subject), family = binomial("logit"), data =d)
summary(fit)

#--------------------#
# Reaction Time      #
#--------------------#

d <- S_M

# Subject as factor
d$subject <- factor(d$subject)

# Create logRT column
d$logRT = log(d$rt)

# Rt affecred by Net Val vs. Abs(Net Val)
fit <- lmer(logRT ~ summedVal + abs(summedVal) + (1 + summedVal + abs(summedVal) | subject), data = d)
summary(fit)
Anova(fit)

# Rt affecred by multnum and net/absNet vals
fit <- lmer(logRT ~ summedVal*factor(multNum) + abs(summedVal) * factor(multNum) + (1 + summedVal*factor(multNum) + abs(summedVal) * factor(multNum) | subject), data = d)
summary(fit)
Anova(fit)

#------------------------------#
# Attrib. Effects on Choice/RT #
#------------------------------#

# Choice affected by face/house?
fit <- glmer(choice ~ faceVal * mult2Face + houseVal * mult1House + (1 + faceVal * mult2Face + houseVal * mult1House | subject), family = binomial("logit"), data = d)
summary(fit)

# Simpler version
fit <- glmer(choice ~ faceTotal + houseTotal + (1 + faceTotal + houseTotal | subject), family = binomial("logit"), data = d)
summary(fit)

# Accuracy affected by face/house
fit <- glmer(correct ~ abs(faceVal)*mult2Face + abs(houseVal)*mult1House + (1 + abs(faceVal)*mult2Face + abs(houseVal)*mult1House | subject), family = binomial("logit"), control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=2e5)), data = d)
summary(fit)

#--------------------#
# Fixations          #
#--------------------#

# Fixation Bias predictive of choice for house or face?
d <- S_M
d$subject <- factor(d$subject)

# Number of swaps affeced by net value (yes)
fit <- lmer(swapAmount ~ summedVal + abs(summedVal) + (1 + summedVal + abs(summedVal) | subject), data =d)
summary(fit)
Anova(fit)

# Choice affected by fixation time on house/face (no)
fit <- glmer(choice ~ total_0_face + total_1_house + (1 + total_0_face + total_1_house|subject), family = binomial("logit"), data = d)
summary(fit)

# Fixation time on Face affected by what?
fit <- lmer(total_0_face ~ faceVal*mult2Face + houseVal*mult1House + (1 + faceVal*mult2Face + houseVal*mult1House | subject), data = d)
summary(fit)
Anova(fit)

# Fixation time on House affected by what?
fit <- lmer(total_1_house ~ faceVal*mult2Face + houseVal*mult1House + (1 + faceVal*mult2Face + houseVal*mult1House | subject), data = d)
summary(fit)
Anova(fit)

# Final Fixation value predictive of chioce?
# Last image val
d$lastVal <- d$faceTotal
d$lastMult <- d$mult2Face
for(i in 1:length(d$Trial)){
  if(d$lastImage[i] == 1){
    d$lastVal[i] <- d$houseVal[i]
    d$lastMult[i] <- d$mult1House[i]
  }
}

load("Data/S_M_K.Rdata")
d<- S_M_K
d$subject <- factor(d$subject) 
# delete all rows but final fix
d <- d[ which(d$revFixNum==1), ] # only final fixations
fit <- glmer(choice ~ fixDur + (1 + fixDur[revFixNum ==1]| subject), family = binomial("logit"), data = d)
summary(fit)

fit <- glmer(choice ~ roi + (1 + roi | subject), family = binomial("logit"), data = d)
summary(fit)

#final fix affected by total value (moreso for absolute)
fit <- glmer(roi ~ totValFace + totValHouse + (1 + totValFace + totValHouse | subject), family = binomial("logit"), data = d)
summary(fit)

# Second fixation
# make the data
d<- S_M_K
d$subject <- factor(d$subject) 
# delete all rows but selected
d <- d[ which(d$fixNum==2), ] # only 2nd fixations
d <- d[ which(d$revFixNum==1), ] # only final fixations

# Final fix item just based on value
d$roi <- factor(d$roi)
fit <- glmer(roi ~ abs(totValFace) + abs(totValHouse) + (abs(totValFace) + abs(totValHouse) | subject), family = binomial("logit"), data = d)
summary(fit)

# final fix roi as predicted by abs(facetotal) + abs(houseTotal) + abs(faceVal)*faceMult + abs(houseVal)*houseMult
d$roi <- factor(d$roi)
fit <- glmer(roi ~ abs(faceVal)*multFace + abs(houseVal)*multHouse + (abs(faceVal)*multFace + abs(houseVal)*multHouse | subject), family = binomial("logit"), data = d)
summary(fit)

# Table for glmer
sjt.glmer(fit, depvar.labels = "Final Fixation Attribute (Face 0, House 1)", exp.coef = FALSE, 
          digits.est = 3, show.ci = FALSE, show.se = TRUE)

# fix and non fix items

# Unweighted Val
d$valFixItem <- d$faceVal
d$valNonFixItem <- d$houseVal
for(i in 1:length(d$trial)){
  if(d$roi[i] == 1){
    d$valFixItem[i] <- d$houseVal[i]
    d$valNonFixItem[i] <- d$faceVal[i]
  }
}

# Weighted Val
d$valFixItem <- d$totValFace
d$valNonFixItem <- d$totValHouse
for(i in 1:length(d$trial)){
  if(d$roi[i] == 1){
    d$valFixItem[i] <- d$totValHouse[i]
    d$valNonFixItem[i] <- d$totValFace[i]
  }
}

#log(fixation duration) ~ abs(valFixItem) + abs(valNonFixItem) + valFixItem + valNonFixItem
# Look at weighting and unweighted values
fit <- lmer(log(fixDur) ~ abs(valFixItem) + abs(valNonFixItem) + valFixItem + valNonFixItem + (1 + abs(valFixItem) + abs(valNonFixItem) + valFixItem + valNonFixItem | subject), data = d)
summary(fit)
Anova(fit)

fit1 <- lmer(log(fixDur) ~ abs(valFixItem) + abs(valNonFixItem) + valFixItem + valNonFixItem + (1 + abs(valFixItem) + abs(valNonFixItem) + valFixItem + valNonFixItem | subject), data = d)
summary(fit1)
library(car)
Anova(fit1)

sjp.glmer(fit1, type = "eff", show.ci = TRUE)
sjp.lmer(fit1, type = "ri.pc")
sjp.lmer(fit1,
         facet.grid = FALSE,
         sort.est = "sort.all",
         y.offset = .4)

sjp.lmer(fit1, type = "pred", vars = "abs(valFixItem)")
sjp.lmer(fit1, type = "rs.ri", sample.n = 15)
sjp.lmer(fit1, type = "fe.slope")
sjp.lmer(fit1, type = "fe.std", p.kr = F)


summary(fit1)

# https://strengejacke.wordpress.com/2015/06/05/beautiful-table-outputs-summarizing-mixed-effects-models-rstats/
.Deprecated(p_value, package = "sjPlot", 'get_model_pval')
# Create Plot
sjt.lmer(fit, fit1, p.kr = FALSE, show.ci = FALSE, show.std = TRUE,
         depvar.labels = c("Base Value", "Weighted Value"), show.header = TRUE,
         digits.est = 3,
         file = "lmer_01")

# Plot random Effects
sjp.lmer(fit, facet.grid = FALSE,
         sort.est = "sort.all",
         y.offset = .4)

# If final fix is house, what about values
d$roi <- factor(d$roi)
fit <- glmer(roi ~ faceVal*multFace + houseVal*multHouse + (faceVal*multFace + houseVal*multHouse | subject), family = binomial("logit"), data = d)
summary(fit)

# Difference in value between final fix item and non-observed item
# dif column
d$dif <- 0
d$dif[d$finalFix == 0] <- abs(d$totValFace) - abs(d$totValHouse)
d$dif[d$finalFix == 1] <- abs(d$totValHouse) - abs(d$totValFace)
fit <- glmer(finalFix ~ dif + (1 + dif | subject), family = binomial("logit"), data = d)
summary(fit)

fit <- glmer(finalFix ~ totValFace * totValHouse + (1 + totValFace * totValHouse | subject), family = binomial("logit"), data = d)
summary(fit)

# Reload Data
d <- S_M
d$subject <- factor(d$subject)

# Bias to weigh the face value more than justified?
fit <- glmer(choice ~ faceTotal * total_0_face + houseTotal * total_1_house + (1 + faceTotal * total_0_face + houseTotal * total_1_house | subject), family = binomial("logit"), data = d)
summary(fit)
# turn into dataframe
fitDf <- as.data.frame.matrix(coef(summary(fit))) 
names(fitDf)[names(fitDf) == "Std. Error"] <- 'se'
names(fitDf)[names(fitDf) == "z value"] <- 'z'
names(fitDf)[names(fitDf) == "Pr(>|z|)"] <- 'p'
fitDf$'Fixed Effects'<-rownames(fitDf)

# remove intercept
fitDf = fitDf[-1,]
fitDf[1,5] = "WFV"
fitDf[2,5] = "TFD"
fitDf[3,5] = "WHV"
fitDf[4,5] = "THD"
fitDf[5,5] = "WFV:TFD"
fitDf[6,5] = "WHV:THD"

# *'s for significance
fitDf$star <- ""
fitDf$star[fitDf$p <= .05]  <- "*"
fitDf$star[fitDf$p <= .01]  <- "**"
fitDf$star[fitDf$p <= .001] <- "***"

# Bar Plot
positions <- c("WFV", "WHV", "TFD", "THD", "WFV:TFD", "WHV:THD")

ggplot(fitDf, aes(`Fixed Effects`, z, fill=`Fixed Effects`)) + 
  geom_bar(stat = "identity", width = 0.5) + 
  geom_errorbar(aes(ymin=z-se, ymax=z+se), width=0.4) +
  geom_text(aes(label=star), colour="black", vjust=0, size=6) +
  scale_x_discrete(limits = positions) +
  theme_minimal() +
  theme(axis.title.x=element_text(size=14),
      axis.title.y = element_text(size = 14))+
  theme(legend.position="none")

setwd("/Users/djw/Dropbox/PHD/PRESENTATIONS/2017_SNE/Plots/")
ggsave("fixedEffects.pdf", width = 20, height = 12, units = "cm")

# plot SJ
library(sjPlot)
library(sjmisc)

sjp.glmer(fit1, type = "fe.std", p.kr = F)

# perecpt amb.
# fit 4 = multNum as continuous
# fit 5 = multNum as factor
fit5 <- glmer(correct ~ faceVal + houseVal + abs(faceVal) + abs(houseVal) + factor(multNum) + multDif + total_0_face + total_1_house + total_0_face:faceVal + total_0_face:abs(faceVal) + total_1_house:houseVal + total_1_house:abs(houseVal) + (faceVal + houseVal + abs(faceVal) + abs(houseVal) + factor(multNum) + multDif + total_0_face + total_1_house + total_0_face:faceVal + total_0_face:abs(faceVal) + total_1_house:houseVal + total_1_house:abs(houseVal) | subject), family = binomial("logit"), data = d) 
summary(fit5)

# fit 6
fit6 <- glmer(correct ~ abs(faceVal)*mult2Face + abs(houseVal)*mult1House + ( abs(faceVal)*mult2Face + abs(houseVal)*mult1House | subject), family = binomial("logit"), data = d) 
summary(fit6)

# fit 7
fit7 <- glmer(correct ~ abs(faceVal)*mult2Face*total_0_face + abs(houseVal)*mult1House*total_1_house + (abs(faceVal)*mult2Face + abs(houseVal)*mult1House | subject), family = binomial("logit"), data = d)
summary(fit7)

# fit 8
fit8 <- glmer(correct ~ abs(houseTotal)*total_1_house + abs(faceTotal)*total_0_face + (abs(houseTotal)*total_1_house + abs(faceTotal)*total_0_face | subject), family = binomial("logit"), data = d)
summary(fit8)

# Final fix
d$choice < factor(d$choice)
fit2 <- glmer(choice ~ faceVal*multFace + houseVal*multHouse + (faceVal*multFace + houseVal*multHouse | subject), family = binomial("logit"), data = d)
summary(fit2)

# Spend a bit more time looking at houses...perceptually harder?
mean(d$total_0_face)
mean(d$total_1_house)

# Last Fixation Value Predictive of Choice?
fit <- glmer(choice ~ total_0_face + total_1_house + (1 + total_0_face + total_1_house|subject), family = binomial("logit"), data = d)
summary(fit)

# Log rt as determined by first fix net val and mult
load("Data/S_M.Rdata")
d <- S_M
d$subject <- factor(d$subject)
d$firstValRaw <- d$firstVal/d$firstMult
d$logFirst <- log(d$`1_fixation`)
d$absFirstVal <- abs(d$firstVal)
fit <- lmer(logFirst ~  firstValRaw*firstMult + absFirstVal + (1+ firstValRaw*firstMult + absFirstVal | subject), data = d)
summary(fit)
Anova(fit)

# 2nd Fix
d$secondValRaw <- d$secondVal/d$secondMult
d$logSecond <- log(d$`2_fixation`)
d$absSecondVal <- abs(d$secondVal)
fit <- lmer(logSecond ~  secondValRaw*secondMult + absSecondVal + (1+ secondValRaw*secondMult + absSecondVal | subject), data = d)
summary(fit)
Anova(fit) 

fit <- lmer(logSecond ~  absSummedVal + summedVal + multNum + (1+ absSummedVal + summedVal + multNum | subject), data = d)
summary(fit)
Anova(fit) 

library(effects)
plot(allEffects(fit))


```

##Test to see if RT for incorrect is longer than RT for correct?

```{r incorrect-correct, echo=FALSE}
#find subject accruacy (uncleaned)
accuracy = tapply(total_M_clean3$correct==1, total_M_clean3$subject, mean)

#hists of rt based on congruent and incongruent trials
hist(total_M_clean3[total_M_clean3$correct==1, ]$logRT,
   col=rgb(1,0,0,0.5), breaks=seq(-1.5,2.5,0.05), ylim=c(0,300), xlab="log RT", main = "log RT vs Frequency")
hist(total_M_clean3[total_M_clean3$correct==0, ]$logRT,
   col=rgb(0,0,1,0.5), breaks=seq(-1.5,2.5,0.05), ylim=c(0,300), add=T)
legend("topright", c("Correct", "Incorrect"), fill=c(rgb(1,0,0,0.5), rgb(0,0,1,0.5)))

#create rts for each subject based on congruent/incongruent
rt_by_condition = tapply(total_M_clean3$logRT, list(total_M_clean3$subject, total_M_clean3$correct), mean)
#convert to data frame
rt_by_condition = as.data.frame(rt_by_condition) 
names(rt_by_condition) = c("incorrect", "correct")

#get means
mean_rts = apply(rt_by_condition, 2, mean)
#get SE
nsubj = length(rt_by_condition[,1])
sds = apply(rt_by_condition, 2, sd)
se = sds/sqrt(nsubj)

#CREATE A BARPLOT
x=barplot(mean_rts, col=c(rgb(1,0,0,0.5),rgb(0,0,1,0.5)),main="RT
   in each condition",xlab="Condition",ylab="RT",ylim = c(0,1.3))
segments(x, mean_rts-se, x, mean_rts+se)

#Test for SIG
total_M_clean3$correct = as.factor(total_M_clean3$correct)
summary(lme(logRT ~ correct, random = ~1+correct|subject, data=total_M_clean3))
```


#LOOKING AT QUESTIONNAIRE DATA
##Note that this is the for the "cleaned" subjects. Running this with all of the subjects gives a significant effect to Self-Control.

```{r Questionnaire-5-factor-01, echo=FALSE}
#Select dataframe to use
load("Data/S_M_raw.Rdata")
d1 <- S_M_raw
d2 <- read.csv("Data/NS_M_raw.csv")
d2$origNumber <- d2$participant

# JOIN DATA FRAMES
# Create ID for each DF
d1$study <- "Swap"
d2$study <- "NoSwap"

# Remove subject cols (using origNumber)
d1$subject <- NULL
d2$subject <- NULL

# Concat DFs
common_cols <- intersect(colnames(d1), colnames(d2))
d = rbind(
  d1[, common_cols], 
  d2[, common_cols]
)

# Rename origNumber (just realized needs to match survey data)
names(d)[names(d)=="origNumber"] <- "subject"

#import Questionnaire data
setwd("~/Dropbox/PHD/CENDRI/Project/Code/LabSharedFolder/MADE01/CODE/GIT/Behavior_Analysis")
Quest.df <- read.csv("csv_files/QuestionnaireResults.csv")
# need to rename participant ID
names(Quest.df)[names(Quest.df) == "Participant.ID"] <- "subject"
#Quest.df <- Quest.df[(Quest.df$study_version == 2), ]

#mean RT and Final earnings by subject
subject_means <- group_by(d, subject) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T), finalEarnings = mean(finalEarnings, na.rm = T), accuracy = mean(as.numeric(correct)))

subject_info <- group_by(Quest.df, subject) %>%
  dplyr::summarize(gpa = mean(GPA, na.rm = T), effort = mean(Effort, na.rm = T), guess = mean(Guessing, na.rm = T), comparative = mean(Compared_to_others, na.rm = T))

subject_means <- merge(subject_means, Quest.df, by = "subject")
subject_means

# For self report measures
subject_info <- group_by(Quest.df, subject) %>%
  dplyr::summarize(gpa = mean(GPA, na.rm = T), effort = mean(Effort, na.rm = T), guess = mean(Guessing, na.rm = T), comparative = mean(Compared_to_others, na.rm = T))

#mean RT and Accuracy by subject
subject_means <- group_by(d, subject) %>%
  dplyr::summarize(rt = mean(rt, na.rm = T), accuracy = mean(as.numeric(correct), na.rm = T))

subject_means <- merge(subject_means, df_Questionnaire, by = "subject")

#################
# FUNCTION TO PULL DATA OUT OF LM
#################

ggplotRegression <- function (fit) {
  require(ggplot2)
  ggplot(fit$model, aes_string(x=names(fit$model)[2], y=names(fit$model)[1])) +
    geom_point() +
    stat_smooth(method = "lm", col = "red") +
    ggtitle("Testing") +
    labs(title = paste(title, "\n\nAdj R2 = ",signif(summary(fit)$adj.r.squared, 5),
                       "Intercept =",signif(fit$coef[[1]], 5),
                       "Slope =",signif(fit$coef[[2]], 5),
                       "P =",signif(summary(fit)$coef[2,4], 5)))
}

#################
# FIVE FACTOR INDEX OF PERSONALITY
#################

#EXTRAVERSION
#old version

# ggplot(subject_means, aes(x = Extraversion, y = accuracy)) +
#   geom_point() +
#   stat_smooth(method = "lm", col = "red")
# 
# plot(x = subject_means$Extraversion, y = subject_means$accuracy,
#      main = "5 Factor: Performance as related to Extraversion",
#      ylab = "Accuracy",
#      xlab = "Extraversion")
# abline(lm(subject_means$accuracy~subject_means$Extraversion), col="red") # regression line (y~x) 
# 
# #test for significance
# summary(lm(accuracy~Extraversion, subject_means))

#new version

title = "5 Factor: Performance as related to Extraversion"
ggplotRegression(lm(accuracy~Extraversion, data = subject_means))

#NEUROTICISM
title = "5 Factor: Performance as related to Neuroticism"
ggplotRegression(lm(accuracy~Neuroticism, data = subject_means))

#CONSCIENTIOUSNESS
title = "5 Factor: Performance as related to Conscientiousness"
ggplotRegression(lm(accuracy~Conscientiousness, data = subject_means))

#OPENNESS
title = "5 Factor: Performance as related to Openness"
ggplotRegression(lm(accuracy~Openness, data = subject_means))

#AGREEABLENESS
title = "5 Factor: Performance as related to Agreeableness"
ggplotRegression(lm(accuracy~Agreeableness, data = subject_means))


######################
# BARRATT IMPULSIVITY SCALE
######################

#ATTENTION
title = "BIS: Performance as related to Attention"
ggplotRegression(lm(accuracy~Attention, data = subject_means))

#COGNITIVE INSTABILITY
title = "BIS: Performance as related to Cognitive Instability"
ggplotRegression(lm(accuracy~Cognitive_Instability, data = subject_means))

#MOTOR
title = "BIS: Performance as related to Motor"
ggplotRegression(lm(accuracy~Motor, data = subject_means))

#PERSERVERANCE
title = "BIS: Performance as related to Perseverance"
ggplotRegression(lm(accuracy~Perseverance, data = subject_means))

#SELF CONTROL
title = "BIS: Performance as related to Self Control"
ggplotRegression(lm(accuracy~Self_Control, data = subject_means))

#COGNITIVE COMPLEXITY
title = "BIS: Performance as related to Cognitive Complexity"
ggplotRegression(lm(accuracy~Cognitive_Complexity, data = subject_means))


######################
# RATIONAL-EXPERIENTIAL INVENTORY
######################

#RATIONAL ABILITY
#PERSERVERANCE
title = "RII: Performance as related to Rational Ability"
ggplotRegression(lm(accuracy~Rational_Ability, data = subject_means))

#RATIONAL ENGAGEMENT
title = "RII: Performance as related to Rational Engagement"
ggplotRegression(lm(accuracy~Rational_Engagement, data = subject_means))

#EXPERIENTIAL ABILITY
title = "RII: Performance as related to Experiential Ability"
ggplotRegression(lm(accuracy~Experiential_Ability, data = subject_means))

#EXPERIENTIAL ENGAGEMENT
title = "RII: Performance as related to Experiential Engagement"
ggplotRegression(lm(accuracy~Experiential_Engagement, data = subject_means))

```

# MULT DIF and MODELS...
```{r multDif-01, echo=FALSE}
#CREATE NEW COLUMN for multDif
total_M_clean3$multDif = abs(total_M_clean3$mult1House - total_M_clean3$mult2Face)
total_M_clean3$multDifF = as.factor(total_M_clean3$multDif)

#TEST MODEL
ctrl <- lmeControl(opt='optim');

#MODEL 1i: absVal * multNum * multDif (BIC 8590.9)
model.1i <- lme(logRT ~ absVal * multNum * multDif, random = ~1+absVal*multNum|subject, control = ctrl, data=total_M_clean3)
summary(model.1i)

#MODEL 1: absVal + multNum + multDif   (BIC 8651.4)
model.1 <- lme(logRT ~ absVal+multNum+multDif, random = ~1+multDif+multNum+absVal|subject, control=ctrl, data=total_M_clean3)
summary(model.1)

#MODEL 1f: multNum & multDif as FACTORS   (BIC 8734.3)
model.1f <- lme(logRT ~ absVal+multNumF+multDifF, random = ~1+multDifF+multNumF+absVal|subject, control=ctrl, data=total_M_clean3)
summary(model.1f)


#MODEL 2: 
#model.2 <- lme(logRT ~ absVal+multNum+multDif+faceVal+houseVal+mult1House+mult2Face, random = ~1+absVal+multNum+multDif+faceVal+houseVal+mult1House+mult2Face|subject, control=ctrl, data=total_M_clean3)
#summary(model.2)

#MODEL 3:
#model.2 <- lme(logRT ~ absVal+multNum+multDif+faceVal+houseVal+mult1House+mult2Face, random = ~1+absVal+multNum+multDif+faceVal+houseVal+mult1House+mult2Face|subject, control=ctrl, data=total_M_clean3)
#summary(model.2)
```
###TEST TO SEE IF INTERACTION IS SIGNIFICANT
###F-test is used to compare the residual sum of squares of both the models 
drop1(model.1i, test = "F")  *doesn't seem to work with lme (example is with lm)


##MODEL VALIDATION

(i) residuals versus fitted values to verify homogeneity

(ii) a QQ-plot or histogram of the residuals for normality

(iii) residuals versus each explanatory variable to check independence

**Instead of a visual inspection, it is also possible to apply a test for homogeneity. 
Sokal and Rohlf (1995) describe three such tests, namely 
1. the Barlett’s test for homogeneity  *sensitive to non-normality! 
2. Hartley’s Fmax test and the log-anova
3. Scheffe ́-Box test

```{r}
help(t.test)
```




